Command that produces this log: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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> trainable params:
>>> pad_vector: torch.Size([1, 1, 1024])
>>> xlmr.embeddings.word_embeddings.weight: torch.Size([250002, 1024])
>>> xlmr.embeddings.position_embeddings.weight: torch.Size([514, 1024])
>>> xlmr.embeddings.token_type_embeddings.weight: torch.Size([1, 1024])
>>> xlmr.embeddings.LayerNorm.weight: torch.Size([1024])
>>> xlmr.embeddings.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.0.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.0.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.0.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.0.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.1.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.1.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.1.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.1.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.2.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.2.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.2.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.2.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.3.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.3.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.3.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.3.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.4.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.4.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.4.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.4.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.5.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.5.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.5.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.5.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.6.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.6.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.6.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.6.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.7.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.7.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.7.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.7.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.8.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.8.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.8.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.8.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.9.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.9.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.9.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.9.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.10.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.10.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.10.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.10.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.11.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.11.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.11.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.11.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.12.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.12.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.12.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.12.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.13.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.13.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.13.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.13.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.14.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.14.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.14.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.14.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.15.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.15.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.15.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.15.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.16.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.16.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.16.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.16.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.17.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.17.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.17.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.17.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.18.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.18.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.18.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.18.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.19.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.19.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.19.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.19.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.20.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.20.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.20.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.20.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.21.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.21.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.21.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.21.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.22.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.22.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.22.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.22.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.23.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.23.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.23.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.23.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.pooler.dense.weight: torch.Size([1024, 1024])
>>> xlmr.pooler.dense.bias: torch.Size([1024])
>>> upos_embedding.weight: torch.Size([19, 50])
>>> dist_embedding.weight: torch.Size([104, 50])
>>> deprel_embedding.weight: torch.Size([38, 50])
>>> gcn_layer.W.0.weight: torch.Size([450, 50])
>>> gcn_layer.W.0.bias: torch.Size([450])
>>> gcn_layer.W.1.weight: torch.Size([450, 450])
>>> gcn_layer.W.1.bias: torch.Size([450])
>>> trans_rep.weight: torch.Size([1024, 2924])
>>> trans_rep.bias: torch.Size([1024])
>>> mention_label_ffn.layers.0.weight: torch.Size([350, 1024])
>>> mention_label_ffn.layers.0.bias: torch.Size([350])
>>> mention_label_ffn.layers.1.weight: torch.Size([11, 350])
>>> mention_label_ffn.layers.1.bias: torch.Size([11])
>>> trigger_label_ffn.layers.0.weight: torch.Size([450, 1024])
>>> trigger_label_ffn.layers.0.bias: torch.Size([450])
>>> trigger_label_ffn.layers.1.weight: torch.Size([233, 450])
>>> trigger_label_ffn.layers.1.bias: torch.Size([233])
>>> event_type_ffn.layers.0.weight: torch.Size([450, 1024])
>>> event_type_ffn.layers.0.bias: torch.Size([450])
>>> event_type_ffn.layers.1.weight: torch.Size([117, 450])
>>> event_type_ffn.layers.1.bias: torch.Size([117])
>>> role_type_ffn.layers.0.weight: torch.Size([350, 1024])
>>> role_type_ffn.layers.0.bias: torch.Size([350])
>>> role_type_ffn.layers.1.weight: torch.Size([6, 350])
>>> role_type_ffn.layers.1.bias: torch.Size([6])
>>> trigger_crf.transition: torch.Size([235, 235])
>>> mention_crf.transition: torch.Size([13, 13])
n_trainable_params: 564979817, n_nontrainable_params: 0
----------------------------------------------------------------------------------------------------
******************************
Epoch: 0
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 05:02:14.946955: step: 3066/1532, loss: 1.6114366054534912
==================================================
Loss: 4.258
--------------------
Dev Chinese: {'event': {'p': 0.6010989010989011, 'r': 0.7283621837549934, 'f1': 0.658639373871162}, 'argument': {'p': 0.5610900167461685, 'r': 0.46660378989054524, 'f1': 0.509503368790972}, 'combined': 0.33557897980573353, 'epoch': 0}
Test Chinese: {'event': {'p': 0.610923151280812, 'r': 0.7222857142857143, 'f1': 0.6619533909400367}, 'argument': {'p': 0.5784999896757367, 'r': 0.5069289041451089, 'f1': 0.5403548173146043}, 'combined': 0.3576897036321864, 'epoch': 0}
Dev Korean: {'event': {'p': 0.5831578947368421, 'r': 0.7376830892143809, 'f1': 0.6513815402704292}, 'argument': {'p': 0.5649188120961904, 'r': 0.4825177371823049, 'f1': 0.5204770581896017}, 'combined': 0.33902914783896454, 'epoch': 0}
Test Korean: {'event': {'p': 0.6072761194029851, 'r': 0.744, 'f1': 0.6687211093990755}, 'argument': {'p': 0.575397300410659, 'r': 0.5078060322035503, 'f1': 0.5394928380750036}, 'combined': 0.3607702491903722, 'epoch': 0}
Dev Russian: {'event': {'p': 0.6215277777777778, 'r': 0.715046604527297, 'f1': 0.6650154798761609}, 'argument': {'p': 0.5577193118718906, 'r': 0.4748150898368798, 'f1': 0.5129389291668484}, 'combined': 0.3411123281270558, 'epoch': 0}
Test Russian: {'event': {'p': 0.6160443995963673, 'r': 0.6977142857142857, 'f1': 0.6543408360128616}, 'argument': {'p': 0.5800386736391421, 'r': 0.5110250003275764, 'f1': 0.5433491563490387}, 'combined': 0.35553554121231307, 'epoch': 0}
Chinese: {'event': {'p': 0.5316455696202531, 'r': 0.7777777777777778, 'f1': 0.6315789473684209}, 'argument': {'p': 0.5557256235827664, 'r': 0.4500918273645546, 'f1': 0.4973617453069508}, 'combined': 0.3141232075622847, 'epoch': 0}
Korean: {'event': {'p': 0.6226415094339622, 'r': 0.5238095238095238, 'f1': 0.5689655172413793}, 'argument': {'p': 0.5659064919071077, 'r': 0.2200747468527641, 'f1': 0.31690763546798034}, 'combined': 0.18030951673178192, 'epoch': 0}
Russian: {'event': {'p': 0.625, 'r': 0.5555555555555556, 'f1': 0.5882352941176471}, 'argument': {'p': 0.4232804232804233, 'r': 0.272108843537415, 'f1': 0.3312629399585922}, 'combined': 0.19486055291681895, 'epoch': 0}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6010989010989011, 'r': 0.7283621837549934, 'f1': 0.658639373871162}, 'argument': {'p': 0.5610900167461685, 'r': 0.46660378989054524, 'f1': 0.509503368790972}, 'combined': 0.33557897980573353, 'epoch': 0}
Test for Chinese: {'event': {'p': 0.610923151280812, 'r': 0.7222857142857143, 'f1': 0.6619533909400367}, 'argument': {'p': 0.5784999896757367, 'r': 0.5069289041451089, 'f1': 0.5403548173146043}, 'combined': 0.3576897036321864, 'epoch': 0}
Chinese: {'event': {'p': 0.5316455696202531, 'r': 0.7777777777777778, 'f1': 0.6315789473684209}, 'argument': {'p': 0.5557256235827664, 'r': 0.4500918273645546, 'f1': 0.4973617453069508}, 'combined': 0.3141232075622847, 'epoch': 0}
--------------------
Dev for Korean: {'event': {'p': 0.5831578947368421, 'r': 0.7376830892143809, 'f1': 0.6513815402704292}, 'argument': {'p': 0.5649188120961904, 'r': 0.4825177371823049, 'f1': 0.5204770581896017}, 'combined': 0.33902914783896454, 'epoch': 0}
Test for Korean: {'event': {'p': 0.6072761194029851, 'r': 0.744, 'f1': 0.6687211093990755}, 'argument': {'p': 0.575397300410659, 'r': 0.5078060322035503, 'f1': 0.5394928380750036}, 'combined': 0.3607702491903722, 'epoch': 0}
Korean: {'event': {'p': 0.6226415094339622, 'r': 0.5238095238095238, 'f1': 0.5689655172413793}, 'argument': {'p': 0.5659064919071077, 'r': 0.2200747468527641, 'f1': 0.31690763546798034}, 'combined': 0.18030951673178192, 'epoch': 0}
--------------------
Dev for Russian: {'event': {'p': 0.6215277777777778, 'r': 0.715046604527297, 'f1': 0.6650154798761609}, 'argument': {'p': 0.5577193118718906, 'r': 0.4748150898368798, 'f1': 0.5129389291668484}, 'combined': 0.3411123281270558, 'epoch': 0}
Test for Russian: {'event': {'p': 0.6160443995963673, 'r': 0.6977142857142857, 'f1': 0.6543408360128616}, 'argument': {'p': 0.5800386736391421, 'r': 0.5110250003275764, 'f1': 0.5433491563490387}, 'combined': 0.35553554121231307, 'epoch': 0}
Russian: {'event': {'p': 0.625, 'r': 0.5555555555555556, 'f1': 0.5882352941176471}, 'argument': {'p': 0.4232804232804233, 'r': 0.272108843537415, 'f1': 0.3312629399585922}, 'combined': 0.19486055291681895, 'epoch': 0}
******************************
Epoch: 1
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 05:28:40.577899: step: 3066/1532, loss: 1.5573700666427612
==================================================
Loss: 1.794
--------------------
Dev Chinese: {'event': {'p': 0.6054945054945055, 'r': 0.7336884154460719, 'f1': 0.6634557495484648}, 'argument': {'p': 0.6470675955897014, 'r': 0.6120780144948508, 'f1': 0.6290866536490662}, 'combined': 0.4173711573276767, 'epoch': 1}
Test Chinese: {'event': {'p': 0.6186563557274045, 'r': 0.7314285714285714, 'f1': 0.6703325477873789}, 'argument': {'p': 0.6563430805633234, 'r': 0.6413088971794797, 'f1': 0.648738898236202}, 'combined': 0.43487079850345045, 'epoch': 1}
Dev Korean: {'event': {'p': 0.5915789473684211, 'r': 0.748335552596538, 'f1': 0.660787771898883}, 'argument': {'p': 0.6415419082291254, 'r': 0.6210721156532867, 'f1': 0.6311410814192205}, 'combined': 0.4170503089448583, 'epoch': 1}
Test Korean: {'event': {'p': 0.6194029850746269, 'r': 0.7588571428571429, 'f1': 0.682074987159733}, 'argument': {'p': 0.6622777395089754, 'r': 0.6490769332147086, 'f1': 0.6556108931293423}, 'combined': 0.44717579151297726, 'epoch': 1}
Dev Russian: {'event': {'p': 0.6192129629629629, 'r': 0.7123834886817576, 'f1': 0.6625386996904025}, 'argument': {'p': 0.6434996961110112, 'r': 0.6151241489730818, 'f1': 0.6289920606236021}, 'combined': 0.4167315819611482, 'epoch': 1}
Test Russian: {'event': {'p': 0.6236125126135217, 'r': 0.7062857142857143, 'f1': 0.662379421221865}, 'argument': {'p': 0.6612263667934108, 'r': 0.6377017454425467, 'f1': 0.6492510313152632}, 'combined': 0.430050522350303, 'epoch': 1}
Chinese: {'event': {'p': 0.5443037974683544, 'r': 0.7962962962962963, 'f1': 0.6466165413533835}, 'argument': {'p': 0.5999743194658449, 'r': 0.5850989231154521, 'f1': 0.592443261062508}, 'combined': 0.3830836124163586, 'epoch': 1}
Korean: {'event': {'p': 0.6415094339622641, 'r': 0.5396825396825397, 'f1': 0.5862068965517241}, 'argument': {'p': 0.6294047619047619, 'r': 0.30596064814814816, 'f1': 0.4117601246105919}, 'combined': 0.24137662477172628, 'epoch': 1}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.4546514091968637, 'r': 0.5001165501165501, 'f1': 0.4763014763014763}, 'combined': 0.26616847205082506, 'epoch': 1}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6054945054945055, 'r': 0.7336884154460719, 'f1': 0.6634557495484648}, 'argument': {'p': 0.6470675955897014, 'r': 0.6120780144948508, 'f1': 0.6290866536490662}, 'combined': 0.4173711573276767, 'epoch': 1}
Test for Chinese: {'event': {'p': 0.6186563557274045, 'r': 0.7314285714285714, 'f1': 0.6703325477873789}, 'argument': {'p': 0.6563430805633234, 'r': 0.6413088971794797, 'f1': 0.648738898236202}, 'combined': 0.43487079850345045, 'epoch': 1}
Chinese: {'event': {'p': 0.5443037974683544, 'r': 0.7962962962962963, 'f1': 0.6466165413533835}, 'argument': {'p': 0.5999743194658449, 'r': 0.5850989231154521, 'f1': 0.592443261062508}, 'combined': 0.3830836124163586, 'epoch': 1}
--------------------
Dev for Korean: {'event': {'p': 0.5915789473684211, 'r': 0.748335552596538, 'f1': 0.660787771898883}, 'argument': {'p': 0.6415419082291254, 'r': 0.6210721156532867, 'f1': 0.6311410814192205}, 'combined': 0.4170503089448583, 'epoch': 1}
Test for Korean: {'event': {'p': 0.6194029850746269, 'r': 0.7588571428571429, 'f1': 0.682074987159733}, 'argument': {'p': 0.6622777395089754, 'r': 0.6490769332147086, 'f1': 0.6556108931293423}, 'combined': 0.44717579151297726, 'epoch': 1}
Korean: {'event': {'p': 0.6415094339622641, 'r': 0.5396825396825397, 'f1': 0.5862068965517241}, 'argument': {'p': 0.6294047619047619, 'r': 0.30596064814814816, 'f1': 0.4117601246105919}, 'combined': 0.24137662477172628, 'epoch': 1}
--------------------
Dev for Russian: {'event': {'p': 0.6192129629629629, 'r': 0.7123834886817576, 'f1': 0.6625386996904025}, 'argument': {'p': 0.6434996961110112, 'r': 0.6151241489730818, 'f1': 0.6289920606236021}, 'combined': 0.4167315819611482, 'epoch': 1}
Test for Russian: {'event': {'p': 0.6236125126135217, 'r': 0.7062857142857143, 'f1': 0.662379421221865}, 'argument': {'p': 0.6612263667934108, 'r': 0.6377017454425467, 'f1': 0.6492510313152632}, 'combined': 0.430050522350303, 'epoch': 1}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.4546514091968637, 'r': 0.5001165501165501, 'f1': 0.4763014763014763}, 'combined': 0.26616847205082506, '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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==================================================
Loss: 1.446
--------------------
Dev Chinese: {'event': {'p': 0.6032967032967033, 'r': 0.7310252996005326, 'f1': 0.6610475617098133}, 'argument': {'p': 0.6225007707998452, 'r': 0.6413923559980641, 'f1': 0.6318053758318516}, 'combined': 0.4176534031687977, 'epoch': 2}
Test Chinese: {'event': {'p': 0.6268728854519091, 'r': 0.7411428571428571, 'f1': 0.67923540193768}, 'argument': {'p': 0.6478581214639699, 'r': 0.6560644721980222, 'f1': 0.6519354731384328}, 'combined': 0.44281765313461496, 'epoch': 2}
Dev Korean: {'event': {'p': 0.5915789473684211, 'r': 0.748335552596538, 'f1': 0.660787771898883}, 'argument': {'p': 0.6200839386442306, 'r': 0.6493119705600211, 'f1': 0.634361464684569}, 'combined': 0.41917829882742835, 'epoch': 2}
Test Korean: {'event': {'p': 0.6231343283582089, 'r': 0.7634285714285715, 'f1': 0.6861838726245506}, 'argument': {'p': 0.6523911856943192, 'r': 0.6680133692303383, 'f1': 0.6601098616123823}, 'combined': 0.45295674119884066, 'epoch': 2}
Dev Russian: {'event': {'p': 0.6238425925925926, 'r': 0.7177097203728362, 'f1': 0.6674922600619194}, 'argument': {'p': 0.6237584844190273, 'r': 0.6481094162431935, 'f1': 0.6357008412635742}, 'combined': 0.4243253912582866, 'epoch': 2}
Test Russian: {'event': {'p': 0.6296670030272452, 'r': 0.7131428571428572, 'f1': 0.6688102893890675}, 'argument': {'p': 0.652289077226433, 'r': 0.6538822466404544, 'f1': 0.6530846903194083}, 'combined': 0.436789760728093, 'epoch': 2}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.5949562065841135, 'r': 0.6342921541268648, 'f1': 0.6139948051948052}, 'combined': 0.41548520652280047, 'epoch': 2}
Korean: {'event': {'p': 0.6226415094339622, 'r': 0.5238095238095238, 'f1': 0.5689655172413793}, 'argument': {'p': 0.6512265512265513, 'r': 0.4279488765203051, 'f1': 0.5164900233865751}, 'combined': 0.29386501330615483, 'epoch': 2}
Russian: {'event': {'p': 0.625, 'r': 0.5555555555555556, 'f1': 0.5882352941176471}, 'argument': {'p': 0.4665151515151515, 'r': 0.568920916481892, 'f1': 0.5126540126540127}, 'combined': 0.3015611839141251, 'epoch': 2}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6032967032967033, 'r': 0.7310252996005326, 'f1': 0.6610475617098133}, 'argument': {'p': 0.6225007707998452, 'r': 0.6413923559980641, 'f1': 0.6318053758318516}, 'combined': 0.4176534031687977, 'epoch': 2}
Test for Chinese: {'event': {'p': 0.6268728854519091, 'r': 0.7411428571428571, 'f1': 0.67923540193768}, 'argument': {'p': 0.6478581214639699, 'r': 0.6560644721980222, 'f1': 0.6519354731384328}, 'combined': 0.44281765313461496, 'epoch': 2}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.5949562065841135, 'r': 0.6342921541268648, 'f1': 0.6139948051948052}, 'combined': 0.41548520652280047, 'epoch': 2}
--------------------
Dev for Korean: {'event': {'p': 0.5915789473684211, 'r': 0.748335552596538, 'f1': 0.660787771898883}, 'argument': {'p': 0.6200839386442306, 'r': 0.6493119705600211, 'f1': 0.634361464684569}, 'combined': 0.41917829882742835, 'epoch': 2}
Test for Korean: {'event': {'p': 0.6231343283582089, 'r': 0.7634285714285715, 'f1': 0.6861838726245506}, 'argument': {'p': 0.6523911856943192, 'r': 0.6680133692303383, 'f1': 0.6601098616123823}, 'combined': 0.45295674119884066, 'epoch': 2}
Korean: {'event': {'p': 0.6226415094339622, 'r': 0.5238095238095238, 'f1': 0.5689655172413793}, 'argument': {'p': 0.6512265512265513, 'r': 0.4279488765203051, 'f1': 0.5164900233865751}, 'combined': 0.29386501330615483, 'epoch': 2}
--------------------
Dev for Russian: {'event': {'p': 0.6238425925925926, 'r': 0.7177097203728362, 'f1': 0.6674922600619194}, 'argument': {'p': 0.6237584844190273, 'r': 0.6481094162431935, 'f1': 0.6357008412635742}, 'combined': 0.4243253912582866, 'epoch': 2}
Test for Russian: {'event': {'p': 0.6296670030272452, 'r': 0.7131428571428572, 'f1': 0.6688102893890675}, 'argument': {'p': 0.652289077226433, 'r': 0.6538822466404544, 'f1': 0.6530846903194083}, 'combined': 0.436789760728093, 'epoch': 2}
Russian: {'event': {'p': 0.625, 'r': 0.5555555555555556, 'f1': 0.5882352941176471}, 'argument': {'p': 0.4665151515151515, 'r': 0.568920916481892, 'f1': 0.5126540126540127}, 'combined': 0.3015611839141251, 'epoch': 2}
******************************
Epoch: 3
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 06:21:36.571284: step: 3066/1532, loss: 0.5002714395523071
==================================================
Loss: 1.037
--------------------
Dev Chinese: {'event': {'p': 0.610989010989011, 'r': 0.7403462050599201, 'f1': 0.6694762191450934}, 'argument': {'p': 0.6631285497016173, 'r': 0.62435197277606, 'f1': 0.6431563211745766}, 'combined': 0.43057786221922284, 'epoch': 3}
Test Chinese: {'event': {'p': 0.6244562590623489, 'r': 0.7382857142857143, 'f1': 0.6766169154228855}, 'argument': {'p': 0.6705603185127378, 'r': 0.6595787779843818, 'f1': 0.6650242168119103}, 'combined': 0.449966634260795, 'epoch': 3}
Dev Korean: {'event': {'p': 0.5926315789473684, 'r': 0.7496671105193076, 'f1': 0.6619635508524397}, 'argument': {'p': 0.6648789611782487, 'r': 0.6364323631292882, 'f1': 0.650344741574871}, 'combined': 0.43050451441111387, 'epoch': 3}
Test Korean: {'event': {'p': 0.6194029850746269, 'r': 0.7588571428571429, 'f1': 0.682074987159733}, 'argument': {'p': 0.6785338450941154, 'r': 0.669157408407611, 'f1': 0.6738130089073076}, 'combined': 0.4595909993985129, 'epoch': 3}
Dev Russian: {'event': {'p': 0.6296296296296297, 'r': 0.7243675099866844, 'f1': 0.6736842105263158}, 'argument': {'p': 0.6656136676376596, 'r': 0.6213058019534773, 'f1': 0.6426969881716048}, 'combined': 0.4329748130840285, 'epoch': 3}
Test Russian: {'event': {'p': 0.627648839556004, 'r': 0.7108571428571429, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6734057594548479, 'r': 0.6576686872945847, 'f1': 0.6654441949792039}, 'combined': 0.4436294633194693, 'epoch': 3}
Chinese: {'event': {'p': 0.5316455696202531, 'r': 0.7777777777777778, 'f1': 0.6315789473684209}, 'argument': {'p': 0.6250707735247208, 'r': 0.6199048993633596, 'f1': 0.6224771188627926}, 'combined': 0.39314344349228997, 'epoch': 3}
Korean: {'event': {'p': 0.6415094339622641, 'r': 0.5396825396825397, 'f1': 0.5862068965517241}, 'argument': {'p': 0.6958109458109458, 'r': 0.36260570415499993, 'f1': 0.4767593517593518}, 'combined': 0.2794796199968614, 'epoch': 3}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.5588018548323279, 'r': 0.5860604818973195, 'f1': 0.5721066608997643}, 'combined': 0.31970666344398596, 'epoch': 3}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6032967032967033, 'r': 0.7310252996005326, 'f1': 0.6610475617098133}, 'argument': {'p': 0.6225007707998452, 'r': 0.6413923559980641, 'f1': 0.6318053758318516}, 'combined': 0.4176534031687977, 'epoch': 2}
Test for Chinese: {'event': {'p': 0.6268728854519091, 'r': 0.7411428571428571, 'f1': 0.67923540193768}, 'argument': {'p': 0.6478581214639699, 'r': 0.6560644721980222, 'f1': 0.6519354731384328}, 'combined': 0.44281765313461496, 'epoch': 2}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.5949562065841135, 'r': 0.6342921541268648, 'f1': 0.6139948051948052}, 'combined': 0.41548520652280047, 'epoch': 2}
--------------------
Dev for Korean: {'event': {'p': 0.5915789473684211, 'r': 0.748335552596538, 'f1': 0.660787771898883}, 'argument': {'p': 0.6200839386442306, 'r': 0.6493119705600211, 'f1': 0.634361464684569}, 'combined': 0.41917829882742835, 'epoch': 2}
Test for Korean: {'event': {'p': 0.6231343283582089, 'r': 0.7634285714285715, 'f1': 0.6861838726245506}, 'argument': {'p': 0.6523911856943192, 'r': 0.6680133692303383, 'f1': 0.6601098616123823}, 'combined': 0.45295674119884066, 'epoch': 2}
Korean: {'event': {'p': 0.6226415094339622, 'r': 0.5238095238095238, 'f1': 0.5689655172413793}, 'argument': {'p': 0.6512265512265513, 'r': 0.4279488765203051, 'f1': 0.5164900233865751}, 'combined': 0.29386501330615483, 'epoch': 2}
--------------------
Dev for Russian: {'event': {'p': 0.6296296296296297, 'r': 0.7243675099866844, 'f1': 0.6736842105263158}, 'argument': {'p': 0.6656136676376596, 'r': 0.6213058019534773, 'f1': 0.6426969881716048}, 'combined': 0.4329748130840285, 'epoch': 3}
Test for Russian: {'event': {'p': 0.627648839556004, 'r': 0.7108571428571429, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6734057594548479, 'r': 0.6576686872945847, 'f1': 0.6654441949792039}, 'combined': 0.4436294633194693, 'epoch': 3}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.5588018548323279, 'r': 0.5860604818973195, 'f1': 0.5721066608997643}, 'combined': 0.31970666344398596, 'epoch': 3}
******************************
Epoch: 4
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 06:26:51.233759: step: 2/1532, loss: 0.7518554329872131
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2023-01-23 06:47:39.101785: step: 3044/1532, loss: 6.365200519561768
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2023-01-23 06:47:47.899903: step: 3066/1532, loss: 0.5672343373298645
==================================================
Loss: 0.853
--------------------
Dev Chinese: {'event': {'p': 0.6076923076923076, 'r': 0.7363515312916112, 'f1': 0.6658639373871162}, 'argument': {'p': 0.6818208956440681, 'r': 0.6348161934239804, 'f1': 0.657479497066372}, 'combined': 0.4377918866679154, 'epoch': 4}
Test Chinese: {'event': {'p': 0.624939584340261, 'r': 0.7388571428571429, 'f1': 0.6771406127258445}, 'argument': {'p': 0.687344339611246, 'r': 0.6437406400315421, 'f1': 0.6648283045341605}, 'combined': 0.4501822454897458, 'epoch': 4}
Dev Korean: {'event': {'p': 0.5831578947368421, 'r': 0.7376830892143809, 'f1': 0.6513815402704292}, 'argument': {'p': 0.683020142792561, 'r': 0.6587327792244655, 'f1': 0.6706566455643247}, 'combined': 0.4368533587802891, 'epoch': 4}
Test Korean: {'event': {'p': 0.6208022388059702, 'r': 0.7605714285714286, 'f1': 0.6836158192090396}, 'argument': {'p': 0.6896845328091452, 'r': 0.661994769177469, 'f1': 0.6755560323091817}, 'combined': 0.46182079044864965, 'epoch': 4}
Dev Russian: {'event': {'p': 0.625, 'r': 0.7190412782956058, 'f1': 0.6687306501547987}, 'argument': {'p': 0.6837935739967493, 'r': 0.6469148194441157, 'f1': 0.6648431747173937}, 'combined': 0.44460100847974315, 'epoch': 4}
Test Russian: {'event': {'p': 0.6301715438950555, 'r': 0.7137142857142857, 'f1': 0.6693461950696677}, 'argument': {'p': 0.688309491312286, 'r': 0.6431271643686025, 'f1': 0.6649516963002445}, 'combined': 0.44508288782368993, 'epoch': 4}
Chinese: {'event': {'p': 0.4936708860759494, 'r': 0.7222222222222222, 'f1': 0.5864661654135338}, 'argument': {'p': 0.677975702975703, 'r': 0.6219446531430002, 'f1': 0.6487526123301985}, 'combined': 0.38047145685530437, 'epoch': 4}
Korean: {'event': {'p': 0.6226415094339622, 'r': 0.5238095238095238, 'f1': 0.5689655172413793}, 'argument': {'p': 0.6542797210417347, 'r': 0.3501778788674073, 'f1': 0.45619503485478746}, 'combined': 0.25955924396910324, 'epoch': 4}
Russian: {'event': {'p': 0.5625, 'r': 0.5, 'f1': 0.5294117647058824}, 'argument': {'p': 0.5542798766936698, 'r': 0.5002037911625801, 'f1': 0.5258552676324559}, 'combined': 0.27839396521718257, 'epoch': 4}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6032967032967033, 'r': 0.7310252996005326, 'f1': 0.6610475617098133}, 'argument': {'p': 0.6225007707998452, 'r': 0.6413923559980641, 'f1': 0.6318053758318516}, 'combined': 0.4176534031687977, 'epoch': 2}
Test for Chinese: {'event': {'p': 0.6268728854519091, 'r': 0.7411428571428571, 'f1': 0.67923540193768}, 'argument': {'p': 0.6478581214639699, 'r': 0.6560644721980222, 'f1': 0.6519354731384328}, 'combined': 0.44281765313461496, 'epoch': 2}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.5949562065841135, 'r': 0.6342921541268648, 'f1': 0.6139948051948052}, 'combined': 0.41548520652280047, 'epoch': 2}
--------------------
Dev for Korean: {'event': {'p': 0.5915789473684211, 'r': 0.748335552596538, 'f1': 0.660787771898883}, 'argument': {'p': 0.6200839386442306, 'r': 0.6493119705600211, 'f1': 0.634361464684569}, 'combined': 0.41917829882742835, 'epoch': 2}
Test for Korean: {'event': {'p': 0.6231343283582089, 'r': 0.7634285714285715, 'f1': 0.6861838726245506}, 'argument': {'p': 0.6523911856943192, 'r': 0.6680133692303383, 'f1': 0.6601098616123823}, 'combined': 0.45295674119884066, 'epoch': 2}
Korean: {'event': {'p': 0.6226415094339622, 'r': 0.5238095238095238, 'f1': 0.5689655172413793}, 'argument': {'p': 0.6512265512265513, 'r': 0.4279488765203051, 'f1': 0.5164900233865751}, 'combined': 0.29386501330615483, 'epoch': 2}
--------------------
Dev for Russian: {'event': {'p': 0.6296296296296297, 'r': 0.7243675099866844, 'f1': 0.6736842105263158}, 'argument': {'p': 0.6656136676376596, 'r': 0.6213058019534773, 'f1': 0.6426969881716048}, 'combined': 0.4329748130840285, 'epoch': 3}
Test for Russian: {'event': {'p': 0.627648839556004, 'r': 0.7108571428571429, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6734057594548479, 'r': 0.6576686872945847, 'f1': 0.6654441949792039}, 'combined': 0.4436294633194693, 'epoch': 3}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.5588018548323279, 'r': 0.5860604818973195, 'f1': 0.5721066608997643}, 'combined': 0.31970666344398596, 'epoch': 3}
******************************
Epoch: 5
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 06:52:49.129074: step: 2/1532, loss: 0.3135243356227875
2023-01-23 06:52:49.963015: step: 4/1532, loss: 0.5467085838317871
2023-01-23 06:52:50.756568: step: 6/1532, loss: 0.2052706480026245
2023-01-23 06:52:51.545443: step: 8/1532, loss: 0.21988309919834137
2023-01-23 06:52:52.365628: step: 10/1532, loss: 1.1359177827835083
2023-01-23 06:52:53.160736: step: 12/1532, loss: 0.2175939381122589
2023-01-23 06:52:53.950061: step: 14/1532, loss: 0.22050276398658752
2023-01-23 06:52:54.791323: step: 16/1532, loss: 1.2740943431854248
2023-01-23 06:52:55.573662: step: 18/1532, loss: 0.5994365215301514
2023-01-23 06:52:56.396923: step: 20/1532, loss: 0.4009748697280884
2023-01-23 06:52:57.206547: step: 22/1532, loss: 5.395275115966797
2023-01-23 06:52:58.000044: step: 24/1532, loss: 0.8882443308830261
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2023-01-23 06:53:01.329102: step: 32/1532, loss: 0.3078804910182953
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2023-01-23 06:53:06.249193: step: 44/1532, loss: 0.2693069279193878
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2023-01-23 06:53:07.957863: step: 48/1532, loss: 0.28661373257637024
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==================================================
Loss: 0.712
--------------------
Dev Chinese: {'event': {'p': 0.6065934065934065, 'r': 0.7350199733688415, 'f1': 0.6646598434677904}, 'argument': {'p': 0.6542958773894224, 'r': 0.6571974112137657, 'f1': 0.655743434640284}, 'combined': 0.43584632862304235, 'epoch': 5}
Test Chinese: {'event': {'p': 0.6215563073948768, 'r': 0.7348571428571429, 'f1': 0.6734747316051324}, 'argument': {'p': 0.6693072723378766, 'r': 0.6747896527256272, 'f1': 0.67203728160642}, 'combined': 0.45260012785852644, 'epoch': 5}
Dev Korean: {'event': {'p': 0.5926315789473684, 'r': 0.7496671105193076, 'f1': 0.6619635508524397}, 'argument': {'p': 0.6469815356656944, 'r': 0.6718293772528551, 'f1': 0.6591713761883683}, 'combined': 0.4363474248019416, 'epoch': 5}
Test Korean: {'event': {'p': 0.6180037313432836, 'r': 0.7571428571428571, 'f1': 0.6805341551104263}, 'argument': {'p': 0.6740606296697603, 'r': 0.6868045173340097, 'f1': 0.6803729031248618}, 'combined': 0.4630169987881057, 'epoch': 5}
Dev Russian: {'event': {'p': 0.6296296296296297, 'r': 0.7243675099866844, 'f1': 0.6736842105263158}, 'argument': {'p': 0.6543273236878316, 'r': 0.6646355560940165, 'f1': 0.6594411583724034}, 'combined': 0.44425509616667175, 'epoch': 5}
Test Russian: {'event': {'p': 0.6291624621594349, 'r': 0.7125714285714285, 'f1': 0.6682743837084674}, 'argument': {'p': 0.6768518988961078, 'r': 0.672123628558028, 'f1': 0.6744794772385679}, 'combined': 0.45073735697561323, 'epoch': 5}
Chinese: {'event': {'p': 0.5569620253164557, 'r': 0.8148148148148148, 'f1': 0.6616541353383458}, 'argument': {'p': 0.6736367607927239, 'r': 0.606829809309148, 'f1': 0.6384904950122339}, 'combined': 0.422459876399072, 'epoch': 5}
Korean: {'event': {'p': 0.6415094339622641, 'r': 0.5396825396825397, 'f1': 0.5862068965517241}, 'argument': {'p': 0.7123323830640904, 'r': 0.4056337181337181, 'f1': 0.5169137647013753}, 'combined': 0.30301841379046135, 'epoch': 5}
Russian: {'event': {'p': 0.5625, 'r': 0.5, 'f1': 0.5294117647058824}, 'argument': {'p': 0.5445356968612782, 'r': 0.5853758741258741, 'f1': 0.5642177100008425}, 'combined': 0.2987034935298578, 'epoch': 5}
New best chinese model...
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6065934065934065, 'r': 0.7350199733688415, 'f1': 0.6646598434677904}, 'argument': {'p': 0.6542958773894224, 'r': 0.6571974112137657, 'f1': 0.655743434640284}, 'combined': 0.43584632862304235, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.6215563073948768, 'r': 0.7348571428571429, 'f1': 0.6734747316051324}, 'argument': {'p': 0.6693072723378766, 'r': 0.6747896527256272, 'f1': 0.67203728160642}, 'combined': 0.45260012785852644, 'epoch': 5}
Chinese: {'event': {'p': 0.5569620253164557, 'r': 0.8148148148148148, 'f1': 0.6616541353383458}, 'argument': {'p': 0.6736367607927239, 'r': 0.606829809309148, 'f1': 0.6384904950122339}, 'combined': 0.422459876399072, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5926315789473684, 'r': 0.7496671105193076, 'f1': 0.6619635508524397}, 'argument': {'p': 0.6469815356656944, 'r': 0.6718293772528551, 'f1': 0.6591713761883683}, 'combined': 0.4363474248019416, 'epoch': 5}
Test for Korean: {'event': {'p': 0.6180037313432836, 'r': 0.7571428571428571, 'f1': 0.6805341551104263}, 'argument': {'p': 0.6740606296697603, 'r': 0.6868045173340097, 'f1': 0.6803729031248618}, 'combined': 0.4630169987881057, 'epoch': 5}
Korean: {'event': {'p': 0.6415094339622641, 'r': 0.5396825396825397, 'f1': 0.5862068965517241}, 'argument': {'p': 0.7123323830640904, 'r': 0.4056337181337181, 'f1': 0.5169137647013753}, 'combined': 0.30301841379046135, 'epoch': 5}
--------------------
Dev for Russian: {'event': {'p': 0.6296296296296297, 'r': 0.7243675099866844, 'f1': 0.6736842105263158}, 'argument': {'p': 0.6656136676376596, 'r': 0.6213058019534773, 'f1': 0.6426969881716048}, 'combined': 0.4329748130840285, 'epoch': 3}
Test for Russian: {'event': {'p': 0.627648839556004, 'r': 0.7108571428571429, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6734057594548479, 'r': 0.6576686872945847, 'f1': 0.6654441949792039}, 'combined': 0.4436294633194693, 'epoch': 3}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.5588018548323279, 'r': 0.5860604818973195, 'f1': 0.5721066608997643}, 'combined': 0.31970666344398596, 'epoch': 3}
******************************
Epoch: 6
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 07:39:59.342295: step: 3066/1532, loss: 0.6009718179702759
==================================================
Loss: 0.583
--------------------
Dev Chinese: {'event': {'p': 0.6065934065934065, 'r': 0.7350199733688415, 'f1': 0.6646598434677904}, 'argument': {'p': 0.6490017502584223, 'r': 0.6514036812364328, 'f1': 0.650200497488305}, 'combined': 0.43216216088325626, 'epoch': 6}
Test Chinese: {'event': {'p': 0.626389560173997, 'r': 0.7405714285714285, 'f1': 0.6787117046347211}, 'argument': {'p': 0.6772466339101298, 'r': 0.6726158876953597, 'f1': 0.6749233178418276}, 'combined': 0.4580783555501485, 'epoch': 6}
Dev Korean: {'event': {'p': 0.5873684210526315, 'r': 0.7430093209054593, 'f1': 0.656084656084656}, 'argument': {'p': 0.6472849318678529, 'r': 0.6655777668989009, 'f1': 0.6563039073386804}, 'combined': 0.4305909233333141, 'epoch': 6}
Test Korean: {'event': {'p': 0.6198694029850746, 'r': 0.7594285714285715, 'f1': 0.6825885978428352}, 'argument': {'p': 0.6784480274893392, 'r': 0.683483741593511, 'f1': 0.6809565748178712}, 'combined': 0.4648131935967904, 'epoch': 6}
Dev Russian: {'event': {'p': 0.6226851851851852, 'r': 0.7163781624500666, 'f1': 0.6662538699690402}, 'argument': {'p': 0.6607947880858941, 'r': 0.6558524186910856, 'f1': 0.6583143271696558}, 'combined': 0.43860446813284804, 'epoch': 6}
Test Russian: {'event': {'p': 0.6306760847628657, 'r': 0.7142857142857143, 'f1': 0.669882100750268}, 'argument': {'p': 0.6825715456381911, 'r': 0.6666035660440015, 'f1': 0.6744930624094755}, 'combined': 0.45183082958834103, 'epoch': 6}
Chinese: {'event': {'p': 0.5316455696202531, 'r': 0.7777777777777778, 'f1': 0.6315789473684209}, 'argument': {'p': 0.665007215007215, 'r': 0.5770723766591536, 'f1': 0.6179270581925449}, 'combined': 0.3902697209637125, 'epoch': 6}
Korean: {'event': {'p': 0.6226415094339622, 'r': 0.5238095238095238, 'f1': 0.5689655172413793}, 'argument': {'p': 0.6388661872071483, 'r': 0.341928381885516, 'f1': 0.44544798374076394}, 'combined': 0.2534445424731933, 'epoch': 6}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6103977141477142, 'r': 0.5955099650221601, 'f1': 0.602861939898977}, 'combined': 0.33689343700236957, 'epoch': 6}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6065934065934065, 'r': 0.7350199733688415, 'f1': 0.6646598434677904}, 'argument': {'p': 0.6542958773894224, 'r': 0.6571974112137657, 'f1': 0.655743434640284}, 'combined': 0.43584632862304235, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.6215563073948768, 'r': 0.7348571428571429, 'f1': 0.6734747316051324}, 'argument': {'p': 0.6693072723378766, 'r': 0.6747896527256272, 'f1': 0.67203728160642}, 'combined': 0.45260012785852644, 'epoch': 5}
Chinese: {'event': {'p': 0.5569620253164557, 'r': 0.8148148148148148, 'f1': 0.6616541353383458}, 'argument': {'p': 0.6736367607927239, 'r': 0.606829809309148, 'f1': 0.6384904950122339}, 'combined': 0.422459876399072, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5926315789473684, 'r': 0.7496671105193076, 'f1': 0.6619635508524397}, 'argument': {'p': 0.6469815356656944, 'r': 0.6718293772528551, 'f1': 0.6591713761883683}, 'combined': 0.4363474248019416, 'epoch': 5}
Test for Korean: {'event': {'p': 0.6180037313432836, 'r': 0.7571428571428571, 'f1': 0.6805341551104263}, 'argument': {'p': 0.6740606296697603, 'r': 0.6868045173340097, 'f1': 0.6803729031248618}, 'combined': 0.4630169987881057, 'epoch': 5}
Korean: {'event': {'p': 0.6415094339622641, 'r': 0.5396825396825397, 'f1': 0.5862068965517241}, 'argument': {'p': 0.7123323830640904, 'r': 0.4056337181337181, 'f1': 0.5169137647013753}, 'combined': 0.30301841379046135, 'epoch': 5}
--------------------
Dev for Russian: {'event': {'p': 0.6226851851851852, 'r': 0.7163781624500666, 'f1': 0.6662538699690402}, 'argument': {'p': 0.6607947880858941, 'r': 0.6558524186910856, 'f1': 0.6583143271696558}, 'combined': 0.43860446813284804, 'epoch': 6}
Test for Russian: {'event': {'p': 0.6306760847628657, 'r': 0.7142857142857143, 'f1': 0.669882100750268}, 'argument': {'p': 0.6825715456381911, 'r': 0.6666035660440015, 'f1': 0.6744930624094755}, 'combined': 0.45183082958834103, 'epoch': 6}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6103977141477142, 'r': 0.5955099650221601, 'f1': 0.602861939898977}, 'combined': 0.33689343700236957, 'epoch': 6}
******************************
Epoch: 7
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 08:06:09.973363: step: 3066/1532, loss: 0.12337112426757812
==================================================
Loss: 0.455
--------------------
Dev Chinese: {'event': {'p': 0.6087912087912087, 'r': 0.7376830892143809, 'f1': 0.6670680313064418}, 'argument': {'p': 0.6440424015499439, 'r': 0.6735768903784176, 'f1': 0.6584786375933627}, 'combined': 0.43925004843675236, 'epoch': 7}
Test Chinese: {'event': {'p': 0.626389560173997, 'r': 0.7405714285714285, 'f1': 0.6787117046347211}, 'argument': {'p': 0.6579696566031233, 'r': 0.705111801907339, 'f1': 0.6807255241733896}, 'combined': 0.4620163809000853, 'epoch': 7}
Dev Korean: {'event': {'p': 0.5894736842105263, 'r': 0.7456724367509987, 'f1': 0.6584362139917695}, 'argument': {'p': 0.6363599817235059, 'r': 0.6847786759850771, 'f1': 0.6596820752945245}, 'combined': 0.43435856809516016, 'epoch': 7}
Test Korean: {'event': {'p': 0.6203358208955224, 'r': 0.76, 'f1': 0.6831022085259374}, 'argument': {'p': 0.6553673285242653, 'r': 0.7209703270317498, 'f1': 0.6866053475535874}, 'combined': 0.46902162929957436, 'epoch': 7}
Dev Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Chinese: {'event': {'p': 0.5316455696202531, 'r': 0.7777777777777778, 'f1': 0.6315789473684209}, 'argument': {'p': 0.5953812767766257, 'r': 0.634745328133758, 'f1': 0.6144334776334777}, 'combined': 0.38806324903167, 'epoch': 7}
Korean: {'event': {'p': 0.6226415094339622, 'r': 0.5238095238095238, 'f1': 0.5689655172413793}, 'argument': {'p': 0.6916849816849816, 'r': 0.48033679283679276, 'f1': 0.5669549030204767}, 'combined': 0.32257778964958156, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, 'epoch': 7}
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6065934065934065, 'r': 0.7350199733688415, 'f1': 0.6646598434677904}, 'argument': {'p': 0.6542958773894224, 'r': 0.6571974112137657, 'f1': 0.655743434640284}, 'combined': 0.43584632862304235, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.6215563073948768, 'r': 0.7348571428571429, 'f1': 0.6734747316051324}, 'argument': {'p': 0.6693072723378766, 'r': 0.6747896527256272, 'f1': 0.67203728160642}, 'combined': 0.45260012785852644, 'epoch': 5}
Chinese: {'event': {'p': 0.5569620253164557, 'r': 0.8148148148148148, 'f1': 0.6616541353383458}, 'argument': {'p': 0.6736367607927239, 'r': 0.606829809309148, 'f1': 0.6384904950122339}, 'combined': 0.422459876399072, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5894736842105263, 'r': 0.7456724367509987, 'f1': 0.6584362139917695}, 'argument': {'p': 0.6363599817235059, 'r': 0.6847786759850771, 'f1': 0.6596820752945245}, 'combined': 0.43435856809516016, 'epoch': 7}
Test for Korean: {'event': {'p': 0.6203358208955224, 'r': 0.76, 'f1': 0.6831022085259374}, 'argument': {'p': 0.6553673285242653, 'r': 0.7209703270317498, 'f1': 0.6866053475535874}, 'combined': 0.46902162929957436, 'epoch': 7}
Korean: {'event': {'p': 0.6226415094339622, 'r': 0.5238095238095238, 'f1': 0.5689655172413793}, 'argument': {'p': 0.6916849816849816, 'r': 0.48033679283679276, 'f1': 0.5669549030204767}, 'combined': 0.32257778964958156, 'epoch': 7}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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
2023-01-23 08:11:28.725298: step: 2/1532, loss: 0.43209409713745117
2023-01-23 08:11:29.583842: step: 4/1532, loss: 0.13813936710357666
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2023-01-23 08:32:24.852243: step: 3066/1532, loss: 0.020026400685310364
==================================================
Loss: 0.388
--------------------
Dev Chinese: {'event': {'p': 0.6120879120879121, 'r': 0.7416777629826897, 'f1': 0.6706803130644191}, 'argument': {'p': 0.6472154651008942, 'r': 0.6654064323152684, 'f1': 0.6561848989717308}, 'combined': 0.44009029347050466, 'epoch': 8}
Test Chinese: {'event': {'p': 0.6259062348960851, 'r': 0.74, 'f1': 0.6781880073317623}, 'argument': {'p': 0.6601188379717796, 'r': 0.6828893306750412, 'f1': 0.6713110492585264}, 'combined': 0.4552751027964345, 'epoch': 8}
Dev Korean: {'event': {'p': 0.588421052631579, 'r': 0.7443408788282291, 'f1': 0.6572604350382129}, 'argument': {'p': 0.6374650486677043, 'r': 0.6725972776008048, 'f1': 0.6545600887872335}, 'combined': 0.4302164487149483, 'epoch': 8}
Test Korean: {'event': {'p': 0.6180037313432836, 'r': 0.7571428571428571, 'f1': 0.6805341551104263}, 'argument': {'p': 0.6619629435834451, 'r': 0.6949718774279027, 'f1': 0.67806592117226}, 'combined': 0.4614470187741369, 'epoch': 8}
Dev Russian: {'event': {'p': 0.6238425925925926, 'r': 0.7177097203728362, 'f1': 0.6674922600619194}, 'argument': {'p': 0.646876418588558, 'r': 0.6653031831360582, 'f1': 0.6559604185539553}, 'combined': 0.4378485022917422, 'epoch': 8}
Test Russian: {'event': {'p': 0.6296670030272452, 'r': 0.7131428571428572, 'f1': 0.6688102893890675}, 'argument': {'p': 0.6665105584828409, 'r': 0.6807114470848156, 'f1': 0.6735361579415334}, 'combined': 0.45046791270687764, 'epoch': 8}
Chinese: {'event': {'p': 0.5063291139240507, 'r': 0.7407407407407407, 'f1': 0.6015037593984962}, 'argument': {'p': 0.6576968229510601, 'r': 0.6413902901506207, 'f1': 0.6494412142947706}, 'combined': 0.3906413319066289, 'epoch': 8}
Korean: {'event': {'p': 0.6415094339622641, 'r': 0.5396825396825397, 'f1': 0.5862068965517241}, 'argument': {'p': 0.6693221712125303, 'r': 0.4276224982746722, 'f1': 0.5218444046741763}, 'combined': 0.30590878894693085, 'epoch': 8}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.5602677684073033, 'r': 0.587597903451562, 'f1': 0.5736074771789058}, 'combined': 0.32054535489409447, 'epoch': 8}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6065934065934065, 'r': 0.7350199733688415, 'f1': 0.6646598434677904}, 'argument': {'p': 0.6542958773894224, 'r': 0.6571974112137657, 'f1': 0.655743434640284}, 'combined': 0.43584632862304235, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.6215563073948768, 'r': 0.7348571428571429, 'f1': 0.6734747316051324}, 'argument': {'p': 0.6693072723378766, 'r': 0.6747896527256272, 'f1': 0.67203728160642}, 'combined': 0.45260012785852644, 'epoch': 5}
Chinese: {'event': {'p': 0.5569620253164557, 'r': 0.8148148148148148, 'f1': 0.6616541353383458}, 'argument': {'p': 0.6736367607927239, 'r': 0.606829809309148, 'f1': 0.6384904950122339}, 'combined': 0.422459876399072, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5894736842105263, 'r': 0.7456724367509987, 'f1': 0.6584362139917695}, 'argument': {'p': 0.6363599817235059, 'r': 0.6847786759850771, 'f1': 0.6596820752945245}, 'combined': 0.43435856809516016, 'epoch': 7}
Test for Korean: {'event': {'p': 0.6203358208955224, 'r': 0.76, 'f1': 0.6831022085259374}, 'argument': {'p': 0.6553673285242653, 'r': 0.7209703270317498, 'f1': 0.6866053475535874}, 'combined': 0.46902162929957436, 'epoch': 7}
Korean: {'event': {'p': 0.6226415094339622, 'r': 0.5238095238095238, 'f1': 0.5689655172413793}, 'argument': {'p': 0.6916849816849816, 'r': 0.48033679283679276, 'f1': 0.5669549030204767}, 'combined': 0.32257778964958156, 'epoch': 7}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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
2023-01-23 08:37:34.366262: step: 2/1532, loss: 0.03998377174139023
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2023-01-23 08:58:31.161538: step: 3066/1532, loss: 0.00480406591668725
==================================================
Loss: 0.332
--------------------
Dev Chinese: {'event': {'p': 0.6054945054945055, 'r': 0.7336884154460719, 'f1': 0.6634557495484648}, 'argument': {'p': 0.6947098897506542, 'r': 0.6288898557846411, 'f1': 0.6601633218065657}, 'combined': 0.43798915149357937, 'epoch': 9}
Test Chinese: {'event': {'p': 0.626389560173997, 'r': 0.7405714285714285, 'f1': 0.6787117046347211}, 'argument': {'p': 0.7146826022448987, 'r': 0.650356276307045, 'f1': 0.68100377687482}, 'combined': 0.46220523426539234, 'epoch': 9}
Dev Korean: {'event': {'p': 0.5863157894736842, 'r': 0.7416777629826897, 'f1': 0.6549088771310994}, 'argument': {'p': 0.6917724531289022, 'r': 0.6440813188420765, 'f1': 0.6670755786278703}, 'combined': 0.4368737181607569, 'epoch': 9}
Test Korean: {'event': {'p': 0.6198694029850746, 'r': 0.7594285714285715, 'f1': 0.6825885978428352}, 'argument': {'p': 0.712934974507861, 'r': 0.664506475056668, 'f1': 0.6878693929307961}, 'combined': 0.4695318044196344, 'epoch': 9}
Dev Russian: {'event': {'p': 0.6203703703703703, 'r': 0.7137150466045273, 'f1': 0.6637770897832818}, 'argument': {'p': 0.6938674185895269, 'r': 0.6284767718114563, 'f1': 0.6595552934944404}, 'combined': 0.43779769326689794, 'epoch': 9}
Test Russian: {'event': {'p': 0.6347124117053481, 'r': 0.7188571428571429, 'f1': 0.6741693461950696}, 'argument': {'p': 0.7149680513656936, 'r': 0.6479476114335475, 'f1': 0.6798099893902798}, 'combined': 0.45830705608412214, 'epoch': 9}
Chinese: {'event': {'p': 0.5443037974683544, 'r': 0.7962962962962963, 'f1': 0.6466165413533835}, 'argument': {'p': 0.6650382327952421, 'r': 0.5880916604057099, 'f1': 0.6242025518341308}, 'combined': 0.40361969517094176, 'epoch': 9}
Korean: {'event': {'p': 0.6415094339622641, 'r': 0.5396825396825397, 'f1': 0.5862068965517241}, 'argument': {'p': 0.771062271062271, 'r': 0.4176587301587301, 'f1': 0.5418275418275417}, 'combined': 0.31762304176097267, 'epoch': 9}
Russian: {'event': {'p': 0.625, 'r': 0.5555555555555556, 'f1': 0.5882352941176471}, 'argument': {'p': 0.5405738705738706, 'r': 0.46146549927037733, 'f1': 0.4978969860548808}, 'combined': 0.29288058003228284, 'epoch': 9}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6065934065934065, 'r': 0.7350199733688415, 'f1': 0.6646598434677904}, 'argument': {'p': 0.6542958773894224, 'r': 0.6571974112137657, 'f1': 0.655743434640284}, 'combined': 0.43584632862304235, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.6215563073948768, 'r': 0.7348571428571429, 'f1': 0.6734747316051324}, 'argument': {'p': 0.6693072723378766, 'r': 0.6747896527256272, 'f1': 0.67203728160642}, 'combined': 0.45260012785852644, 'epoch': 5}
Chinese: {'event': {'p': 0.5569620253164557, 'r': 0.8148148148148148, 'f1': 0.6616541353383458}, 'argument': {'p': 0.6736367607927239, 'r': 0.606829809309148, 'f1': 0.6384904950122339}, 'combined': 0.422459876399072, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5894736842105263, 'r': 0.7456724367509987, 'f1': 0.6584362139917695}, 'argument': {'p': 0.6363599817235059, 'r': 0.6847786759850771, 'f1': 0.6596820752945245}, 'combined': 0.43435856809516016, 'epoch': 7}
Test for Korean: {'event': {'p': 0.6203358208955224, 'r': 0.76, 'f1': 0.6831022085259374}, 'argument': {'p': 0.6553673285242653, 'r': 0.7209703270317498, 'f1': 0.6866053475535874}, 'combined': 0.46902162929957436, 'epoch': 7}
Korean: {'event': {'p': 0.6226415094339622, 'r': 0.5238095238095238, 'f1': 0.5689655172413793}, 'argument': {'p': 0.6916849816849816, 'r': 0.48033679283679276, 'f1': 0.5669549030204767}, 'combined': 0.32257778964958156, 'epoch': 7}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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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2023-01-23 09:24:28.552528: step: 3066/1532, loss: 0.07463278621435165
==================================================
Loss: 0.259
--------------------
Dev Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6709019658961448, 'r': 0.660481151337107, 'f1': 0.66565077645007}, 'combined': 0.4448358590364706, 'epoch': 10}
Test Chinese: {'event': {'p': 0.6225229579507008, 'r': 0.736, 'f1': 0.67452212621105}, 'argument': {'p': 0.6766645146273007, 'r': 0.6706538288123673, 'f1': 0.6736457642188741}, 'combined': 0.45438897319398264, 'epoch': 10}
Dev Korean: {'event': {'p': 0.5852631578947368, 'r': 0.7403462050599201, 'f1': 0.6537330981775425}, 'argument': {'p': 0.6682000253607236, 'r': 0.6740188935061, 'f1': 0.6710968462799175}, 'combined': 0.4387182204957485, 'epoch': 10}
Test Korean: {'event': {'p': 0.617070895522388, 'r': 0.756, 'f1': 0.6795069337442219}, 'argument': {'p': 0.678111186324095, 'r': 0.6831460989461435, 'f1': 0.6806193312634241}, 'combined': 0.4624855548338521, 'epoch': 10}
Dev Russian: {'event': {'p': 0.6238425925925926, 'r': 0.7177097203728362, 'f1': 0.6674922600619194}, 'argument': {'p': 0.6764959572513929, 'r': 0.6648234838704611, 'f1': 0.6706089322734201}, 'combined': 0.4476262718208958, 'epoch': 10}
Test Russian: {'event': {'p': 0.6331987891019173, 'r': 0.7171428571428572, 'f1': 0.6725616291532691}, 'argument': {'p': 0.6807529090364076, 'r': 0.663395255400481, 'f1': 0.671962008212127}, 'combined': 0.4519358629722506, 'epoch': 10}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.6422241280731847, 'r': 0.5626095667417982, 'f1': 0.5997864103590976}, 'combined': 0.40587050325051716, 'epoch': 10}
Korean: {'event': {'p': 0.6226415094339622, 'r': 0.5238095238095238, 'f1': 0.5689655172413793}, 'argument': {'p': 0.6885382059800664, 'r': 0.4170020120724346, 'f1': 0.5194235588972431}, 'combined': 0.295534093855328, 'epoch': 10}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.5880021367521368, 'r': 0.5736606212215968, 'f1': 0.5807428511132214}, 'combined': 0.3245327697397415, 'epoch': 10}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6065934065934065, 'r': 0.7350199733688415, 'f1': 0.6646598434677904}, 'argument': {'p': 0.6542958773894224, 'r': 0.6571974112137657, 'f1': 0.655743434640284}, 'combined': 0.43584632862304235, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.6215563073948768, 'r': 0.7348571428571429, 'f1': 0.6734747316051324}, 'argument': {'p': 0.6693072723378766, 'r': 0.6747896527256272, 'f1': 0.67203728160642}, 'combined': 0.45260012785852644, 'epoch': 5}
Chinese: {'event': {'p': 0.5569620253164557, 'r': 0.8148148148148148, 'f1': 0.6616541353383458}, 'argument': {'p': 0.6736367607927239, 'r': 0.606829809309148, 'f1': 0.6384904950122339}, 'combined': 0.422459876399072, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5894736842105263, 'r': 0.7456724367509987, 'f1': 0.6584362139917695}, 'argument': {'p': 0.6363599817235059, 'r': 0.6847786759850771, 'f1': 0.6596820752945245}, 'combined': 0.43435856809516016, 'epoch': 7}
Test for Korean: {'event': {'p': 0.6203358208955224, 'r': 0.76, 'f1': 0.6831022085259374}, 'argument': {'p': 0.6553673285242653, 'r': 0.7209703270317498, 'f1': 0.6866053475535874}, 'combined': 0.46902162929957436, 'epoch': 7}
Korean: {'event': {'p': 0.6226415094339622, 'r': 0.5238095238095238, 'f1': 0.5689655172413793}, 'argument': {'p': 0.6916849816849816, 'r': 0.48033679283679276, 'f1': 0.5669549030204767}, 'combined': 0.32257778964958156, 'epoch': 7}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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
2023-01-23 09:29:34.706921: step: 2/1532, loss: 0.3899801969528198
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2023-01-23 09:50:30.931546: step: 3064/1532, loss: 0.28478574752807617
2023-01-23 09:50:31.511681: step: 3066/1532, loss: 0.33315926790237427
==================================================
Loss: 0.227
--------------------
Dev Chinese: {'event': {'p': 0.6142857142857143, 'r': 0.7443408788282291, 'f1': 0.6730885009030705}, 'argument': {'p': 0.6715595047872834, 'r': 0.6486597737122537, 'f1': 0.6599110367555281}, 'combined': 0.44417853045916944, 'epoch': 11}
Test Chinese: {'event': {'p': 0.6201063315611407, 'r': 0.7331428571428571, 'f1': 0.6719036396962557}, 'argument': {'p': 0.6790783426221775, 'r': 0.6665586326523593, 'f1': 0.672760246395162}, 'combined': 0.45203005819585906, 'epoch': 11}
Dev Korean: {'event': {'p': 0.5863157894736842, 'r': 0.7416777629826897, 'f1': 0.6549088771310994}, 'argument': {'p': 0.674192650353494, 'r': 0.6590367604615736, 'f1': 0.6665285608188558}, 'combined': 0.4365154713416845, 'epoch': 11}
Test Korean: {'event': {'p': 0.6198694029850746, 'r': 0.7594285714285715, 'f1': 0.6825885978428352}, 'argument': {'p': 0.6769914230394771, 'r': 0.6808756127767944, 'f1': 0.6789279625299148}, 'combined': 0.4634284859795875, 'epoch': 11}
Dev Russian: {'event': {'p': 0.6284722222222222, 'r': 0.7230359520639148, 'f1': 0.6724458204334366}, 'argument': {'p': 0.672410337799243, 'r': 0.6517440530542887, 'f1': 0.6619159244571154}, 'combined': 0.44510259687952164, 'epoch': 11}
Test Russian: {'event': {'p': 0.6296670030272452, 'r': 0.7131428571428572, 'f1': 0.6688102893890675}, 'argument': {'p': 0.6848177746642532, 'r': 0.6618629889213173, 'f1': 0.6731447444142944}, 'combined': 0.4502061313124541, 'epoch': 11}
Chinese: {'event': {'p': 0.5569620253164557, 'r': 0.8148148148148148, 'f1': 0.6616541353383458}, 'argument': {'p': 0.6648455710955712, 'r': 0.5714375156523918, 'f1': 0.6146127946127947}, 'combined': 0.406661097187413, 'epoch': 11}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7181919642857142, 'r': 0.41039540816326525, 'f1': 0.5223214285714285}, 'combined': 0.3151939655172414, 'epoch': 11}
Russian: {'event': {'p': 0.5625, 'r': 0.5, 'f1': 0.5294117647058824}, 'argument': {'p': 0.5635576923076924, 'r': 0.5498123827392121, 'f1': 0.5566001899335233}, 'combined': 0.2946706887883359, 'epoch': 11}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6065934065934065, 'r': 0.7350199733688415, 'f1': 0.6646598434677904}, 'argument': {'p': 0.6542958773894224, 'r': 0.6571974112137657, 'f1': 0.655743434640284}, 'combined': 0.43584632862304235, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.6215563073948768, 'r': 0.7348571428571429, 'f1': 0.6734747316051324}, 'argument': {'p': 0.6693072723378766, 'r': 0.6747896527256272, 'f1': 0.67203728160642}, 'combined': 0.45260012785852644, 'epoch': 5}
Chinese: {'event': {'p': 0.5569620253164557, 'r': 0.8148148148148148, 'f1': 0.6616541353383458}, 'argument': {'p': 0.6736367607927239, 'r': 0.606829809309148, 'f1': 0.6384904950122339}, 'combined': 0.422459876399072, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5894736842105263, 'r': 0.7456724367509987, 'f1': 0.6584362139917695}, 'argument': {'p': 0.6363599817235059, 'r': 0.6847786759850771, 'f1': 0.6596820752945245}, 'combined': 0.43435856809516016, 'epoch': 7}
Test for Korean: {'event': {'p': 0.6203358208955224, 'r': 0.76, 'f1': 0.6831022085259374}, 'argument': {'p': 0.6553673285242653, 'r': 0.7209703270317498, 'f1': 0.6866053475535874}, 'combined': 0.46902162929957436, 'epoch': 7}
Korean: {'event': {'p': 0.6226415094339622, 'r': 0.5238095238095238, 'f1': 0.5689655172413793}, 'argument': {'p': 0.6916849816849816, 'r': 0.48033679283679276, 'f1': 0.5669549030204767}, 'combined': 0.32257778964958156, 'epoch': 7}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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
2023-01-23 09:55:36.607627: step: 2/1532, loss: 0.04059543460607529
2023-01-23 09:55:37.426314: step: 4/1532, loss: 0.00862431526184082
2023-01-23 09:55:38.252125: step: 6/1532, loss: 0.2230650931596756
2023-01-23 09:55:39.064845: step: 8/1532, loss: 0.060476429760456085
2023-01-23 09:55:39.867809: step: 10/1532, loss: 0.18902122974395752
2023-01-23 09:55:40.677607: step: 12/1532, loss: 0.06611781567335129
2023-01-23 09:55:41.484776: step: 14/1532, loss: 0.32389405369758606
2023-01-23 09:55:42.286265: step: 16/1532, loss: 0.24784055352210999
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2023-01-23 10:16:33.053078: step: 3066/1532, loss: 0.35533407330513
==================================================
Loss: 0.215
--------------------
Dev Chinese: {'event': {'p': 0.610989010989011, 'r': 0.7403462050599201, 'f1': 0.6694762191450934}, 'argument': {'p': 0.673480836278722, 'r': 0.653042113638139, 'f1': 0.6631040176814175}, 'combined': 0.44393237065727653, 'epoch': 12}
Test Chinese: {'event': {'p': 0.6215563073948768, 'r': 0.7348571428571429, 'f1': 0.6734747316051324}, 'argument': {'p': 0.6818471110700868, 'r': 0.6755509852154724, 'f1': 0.6786844462360561}, 'combined': 0.45707682527340576, 'epoch': 12}
Dev Korean: {'event': {'p': 0.5873684210526315, 'r': 0.7430093209054593, 'f1': 0.656084656084656}, 'argument': {'p': 0.670231376540917, 'r': 0.6639084390263801, 'f1': 0.667054924519396}, 'combined': 0.4376445007428841, 'epoch': 12}
Test Korean: {'event': {'p': 0.6189365671641791, 'r': 0.7582857142857143, 'f1': 0.6815613764766307}, 'argument': {'p': 0.6848906297417182, 'r': 0.6883578452145923, 'f1': 0.6866198604177199}, 'combined': 0.46797357718249327, 'epoch': 12}
Dev Russian: {'event': {'p': 0.625, 'r': 0.7190412782956058, 'f1': 0.6687306501547987}, 'argument': {'p': 0.6794984323501247, 'r': 0.6490276506303881, 'f1': 0.6639136059200761}, 'combined': 0.4439793773335493, 'epoch': 12}
Test Russian: {'event': {'p': 0.6306760847628657, 'r': 0.7142857142857143, 'f1': 0.669882100750268}, 'argument': {'p': 0.6902795811616451, 'r': 0.6738730759120849, 'f1': 0.6819776689722232}, 'combined': 0.45684463355588373, 'epoch': 12}
Chinese: {'event': {'p': 0.5443037974683544, 'r': 0.7962962962962963, 'f1': 0.6466165413533835}, 'argument': {'p': 0.672455688965123, 'r': 0.5890934134735788, 'f1': 0.6280202910158857}, 'combined': 0.40608830847643745, 'epoch': 12}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7445054945054945, 'r': 0.44238732282210547, 'f1': 0.554995004995005}, 'combined': 0.33491077887629617, 'epoch': 12}
Russian: {'event': {'p': 0.625, 'r': 0.5555555555555556, 'f1': 0.5882352941176471}, 'argument': {'p': 0.5786917919245506, 'r': 0.5645773579751713, 'f1': 0.5715474488143709}, 'combined': 0.3362043816555123, 'epoch': 12}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6065934065934065, 'r': 0.7350199733688415, 'f1': 0.6646598434677904}, 'argument': {'p': 0.6542958773894224, 'r': 0.6571974112137657, 'f1': 0.655743434640284}, 'combined': 0.43584632862304235, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.6215563073948768, 'r': 0.7348571428571429, 'f1': 0.6734747316051324}, 'argument': {'p': 0.6693072723378766, 'r': 0.6747896527256272, 'f1': 0.67203728160642}, 'combined': 0.45260012785852644, 'epoch': 5}
Chinese: {'event': {'p': 0.5569620253164557, 'r': 0.8148148148148148, 'f1': 0.6616541353383458}, 'argument': {'p': 0.6736367607927239, 'r': 0.606829809309148, 'f1': 0.6384904950122339}, 'combined': 0.422459876399072, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5873684210526315, 'r': 0.7430093209054593, 'f1': 0.656084656084656}, 'argument': {'p': 0.670231376540917, 'r': 0.6639084390263801, 'f1': 0.667054924519396}, 'combined': 0.4376445007428841, 'epoch': 12}
Test for Korean: {'event': {'p': 0.6189365671641791, 'r': 0.7582857142857143, 'f1': 0.6815613764766307}, 'argument': {'p': 0.6848906297417182, 'r': 0.6883578452145923, 'f1': 0.6866198604177199}, 'combined': 0.46797357718249327, 'epoch': 12}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7445054945054945, 'r': 0.44238732282210547, 'f1': 0.554995004995005}, 'combined': 0.33491077887629617, 'epoch': 12}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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
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2023-01-23 10:42:42.853630: step: 3066/1532, loss: 0.11717406660318375
==================================================
Loss: 0.179
--------------------
Dev Chinese: {'event': {'p': 0.6076923076923076, 'r': 0.7363515312916112, 'f1': 0.6658639373871162}, 'argument': {'p': 0.6654802332357055, 'r': 0.6448070307239454, 'f1': 0.6549805451080197}, 'combined': 0.4361279246775857, 'epoch': 13}
Test Chinese: {'event': {'p': 0.625422909618173, 'r': 0.7394285714285714, 'f1': 0.6776643100288033}, 'argument': {'p': 0.6837216769068387, 'r': 0.6692637151168578, 'f1': 0.6764154473363071}, 'combined': 0.4583826074119829, 'epoch': 13}
Dev Korean: {'event': {'p': 0.5863157894736842, 'r': 0.7416777629826897, 'f1': 0.6549088771310994}, 'argument': {'p': 0.658915980261509, 'r': 0.6550934074971202, 'f1': 0.6569991337734755}, 'combined': 0.4302745649756918, 'epoch': 13}
Test Korean: {'event': {'p': 0.6217350746268657, 'r': 0.7617142857142857, 'f1': 0.684643040575244}, 'argument': {'p': 0.6846259297627594, 'r': 0.6832419150799506, 'f1': 0.6839332222452928}, 'combined': 0.4682501208284414, 'epoch': 13}
Dev Russian: {'event': {'p': 0.625, 'r': 0.7190412782956058, 'f1': 0.6687306501547987}, 'argument': {'p': 0.6737388457426646, 'r': 0.6500192009861813, 'f1': 0.6616665141567305}, 'combined': 0.4424766781976897, 'epoch': 13}
Test Russian: {'event': {'p': 0.6316851664984864, 'r': 0.7154285714285714, 'f1': 0.6709539121114683}, 'argument': {'p': 0.6950315390105628, 'r': 0.6705637155738019, 'f1': 0.6825784282353159}, 'combined': 0.4579786667473823, 'epoch': 13}
Chinese: {'event': {'p': 0.5443037974683544, 'r': 0.7962962962962963, 'f1': 0.6466165413533835}, 'argument': {'p': 0.6879779099405268, 'r': 0.6083771600300526, 'f1': 0.6457336523125997}, 'combined': 0.41754206089386153, 'epoch': 13}
Korean: {'event': {'p': 0.6415094339622641, 'r': 0.5396825396825397, 'f1': 0.5862068965517241}, 'argument': {'p': 0.7384343597578892, 'r': 0.45126544207426567, 'f1': 0.5601915832646056}, 'combined': 0.3283881694999412, 'epoch': 13}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.5846008828336414, 'r': 0.5703423247157478, 'f1': 0.5773835879838435}, 'combined': 0.32265553446155965, 'epoch': 13}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6065934065934065, 'r': 0.7350199733688415, 'f1': 0.6646598434677904}, 'argument': {'p': 0.6542958773894224, 'r': 0.6571974112137657, 'f1': 0.655743434640284}, 'combined': 0.43584632862304235, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.6215563073948768, 'r': 0.7348571428571429, 'f1': 0.6734747316051324}, 'argument': {'p': 0.6693072723378766, 'r': 0.6747896527256272, 'f1': 0.67203728160642}, 'combined': 0.45260012785852644, 'epoch': 5}
Chinese: {'event': {'p': 0.5569620253164557, 'r': 0.8148148148148148, 'f1': 0.6616541353383458}, 'argument': {'p': 0.6736367607927239, 'r': 0.606829809309148, 'f1': 0.6384904950122339}, 'combined': 0.422459876399072, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5873684210526315, 'r': 0.7430093209054593, 'f1': 0.656084656084656}, 'argument': {'p': 0.670231376540917, 'r': 0.6639084390263801, 'f1': 0.667054924519396}, 'combined': 0.4376445007428841, 'epoch': 12}
Test for Korean: {'event': {'p': 0.6189365671641791, 'r': 0.7582857142857143, 'f1': 0.6815613764766307}, 'argument': {'p': 0.6848906297417182, 'r': 0.6883578452145923, 'f1': 0.6866198604177199}, 'combined': 0.46797357718249327, 'epoch': 12}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7445054945054945, 'r': 0.44238732282210547, 'f1': 0.554995004995005}, 'combined': 0.33491077887629617, 'epoch': 12}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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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2023-01-23 11:08:46.327545: step: 3066/1532, loss: 0.3148682415485382
==================================================
Loss: 0.160
--------------------
Dev Chinese: {'event': {'p': 0.610989010989011, 'r': 0.7403462050599201, 'f1': 0.6694762191450934}, 'argument': {'p': 0.66782168586842, 'r': 0.6495184248483225, 'f1': 0.6585429016486934}, 'combined': 0.44087881194060635, 'epoch': 14}
Test Chinese: {'event': {'p': 0.625422909618173, 'r': 0.7394285714285714, 'f1': 0.6776643100288033}, 'argument': {'p': 0.6943425998307885, 'r': 0.6805979340320903, 'f1': 0.6874015672919768}, 'combined': 0.4658275088116355, 'epoch': 14}
Dev Korean: {'event': {'p': 0.5842105263157895, 'r': 0.7390146471371505, 'f1': 0.652557319223986}, 'argument': {'p': 0.6599733374907224, 'r': 0.6585365305150531, 'f1': 0.6592541511440199}, 'combined': 0.4302011215578261, 'epoch': 14}
Test Korean: {'event': {'p': 0.6217350746268657, 'r': 0.7617142857142857, 'f1': 0.684643040575244}, 'argument': {'p': 0.6870088660626555, 'r': 0.6967372451505287, 'f1': 0.6918388580905483}, 'combined': 0.4736626593912178, 'epoch': 14}
Dev Russian: {'event': {'p': 0.6284722222222222, 'r': 0.7230359520639148, 'f1': 0.6724458204334366}, 'argument': {'p': 0.6689565831531533, 'r': 0.6479265109282788, 'f1': 0.6582736263119242}, 'combined': 0.4426533487150153, 'epoch': 14}
Test Russian: {'event': {'p': 0.6331987891019173, 'r': 0.7171428571428572, 'f1': 0.6725616291532691}, 'argument': {'p': 0.695968062256685, 'r': 0.673368540682009, 'f1': 0.6844818102975241}, 'combined': 0.4603562014594817, 'epoch': 14}
Chinese: {'event': {'p': 0.5569620253164557, 'r': 0.8148148148148148, 'f1': 0.6616541353383458}, 'argument': {'p': 0.6352137445887447, 'r': 0.5879664412722265, 'f1': 0.6106775913642868}, 'combined': 0.40405735368464085, 'epoch': 14}
Korean: {'event': {'p': 0.6415094339622641, 'r': 0.5396825396825397, 'f1': 0.5862068965517241}, 'argument': {'p': 0.6945887445887445, 'r': 0.5380617035546612, 'f1': 0.606386999244142}, 'combined': 0.35546824093622115, 'epoch': 14}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.5456988690322024, 'r': 0.5989377830841246, 'f1': 0.5710802117778863}, 'combined': 0.3191330595229365, 'epoch': 14}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6065934065934065, 'r': 0.7350199733688415, 'f1': 0.6646598434677904}, 'argument': {'p': 0.6542958773894224, 'r': 0.6571974112137657, 'f1': 0.655743434640284}, 'combined': 0.43584632862304235, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.6215563073948768, 'r': 0.7348571428571429, 'f1': 0.6734747316051324}, 'argument': {'p': 0.6693072723378766, 'r': 0.6747896527256272, 'f1': 0.67203728160642}, 'combined': 0.45260012785852644, 'epoch': 5}
Chinese: {'event': {'p': 0.5569620253164557, 'r': 0.8148148148148148, 'f1': 0.6616541353383458}, 'argument': {'p': 0.6736367607927239, 'r': 0.606829809309148, 'f1': 0.6384904950122339}, 'combined': 0.422459876399072, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5842105263157895, 'r': 0.7390146471371505, 'f1': 0.652557319223986}, 'argument': {'p': 0.6599733374907224, 'r': 0.6585365305150531, 'f1': 0.6592541511440199}, 'combined': 0.4302011215578261, 'epoch': 14}
Test for Korean: {'event': {'p': 0.6217350746268657, 'r': 0.7617142857142857, 'f1': 0.684643040575244}, 'argument': {'p': 0.6870088660626555, 'r': 0.6967372451505287, 'f1': 0.6918388580905483}, 'combined': 0.4736626593912178, 'epoch': 14}
Korean: {'event': {'p': 0.6415094339622641, 'r': 0.5396825396825397, 'f1': 0.5862068965517241}, 'argument': {'p': 0.6945887445887445, 'r': 0.5380617035546612, 'f1': 0.606386999244142}, 'combined': 0.35546824093622115, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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
2023-01-23 11:13:57.839094: step: 2/1532, loss: 0.10632260888814926
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2023-01-23 11:34:51.206736: step: 3058/1532, loss: 0.24068082869052887
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2023-01-23 11:34:53.731277: step: 3064/1532, loss: 0.010064676403999329
2023-01-23 11:34:54.276963: step: 3066/1532, loss: 0.5727847218513489
==================================================
Loss: 0.155
--------------------
Dev Chinese: {'event': {'p': 0.6065934065934065, 'r': 0.7350199733688415, 'f1': 0.6646598434677904}, 'argument': {'p': 0.6628070706196243, 'r': 0.6348425976179082, 'f1': 0.6485235156006003}, 'combined': 0.4310475383642761, 'epoch': 15}
Test Chinese: {'event': {'p': 0.6234896085065249, 'r': 0.7371428571428571, 'f1': 0.6755695208169678}, 'argument': {'p': 0.6899979817158367, 'r': 0.6673673680346887, 'f1': 0.6784940209230753}, 'combined': 0.4583698805921797, 'epoch': 15}
Dev Korean: {'event': {'p': 0.5842105263157895, 'r': 0.7390146471371505, 'f1': 0.652557319223986}, 'argument': {'p': 0.6611489870839514, 'r': 0.6486744778936883, 'f1': 0.6548523300641043}, 'combined': 0.42732868099421273, 'epoch': 15}
Test Korean: {'event': {'p': 0.6226679104477612, 'r': 0.7628571428571429, 'f1': 0.6856702619414484}, 'argument': {'p': 0.6896145677554094, 'r': 0.6858919520050428, 'f1': 0.6877482225111459}, 'combined': 0.4715685038789829, 'epoch': 15}
Dev Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.6667647599499162, 'r': 0.6363666655127377, 'f1': 0.6512111651671456}, 'combined': 0.43709777278092005, 'epoch': 15}
Test Russian: {'event': {'p': 0.6316851664984864, 'r': 0.7154285714285714, 'f1': 0.6709539121114683}, 'argument': {'p': 0.6905594657566997, 'r': 0.658535613265193, 'f1': 0.6741674598766995}, 'combined': 0.4523352946225228, 'epoch': 15}
Chinese: {'event': {'p': 0.5316455696202531, 'r': 0.7777777777777778, 'f1': 0.6315789473684209}, 'argument': {'p': 0.6537798621131954, 'r': 0.5835390504811991, 'f1': 0.616665721469215}, 'combined': 0.3894730872437146, 'epoch': 15}
Korean: {'event': {'p': 0.6415094339622641, 'r': 0.5396825396825397, 'f1': 0.5862068965517241}, 'argument': {'p': 0.7922077922077922, 'r': 0.4841269841269842, 'f1': 0.600985221674877}, 'combined': 0.35230168167147957, 'epoch': 15}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.5558108587140845, 'r': 0.47447268426812095, 'f1': 0.511931054078762}, 'combined': 0.2860791184557789, 'epoch': 15}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6065934065934065, 'r': 0.7350199733688415, 'f1': 0.6646598434677904}, 'argument': {'p': 0.6542958773894224, 'r': 0.6571974112137657, 'f1': 0.655743434640284}, 'combined': 0.43584632862304235, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.6215563073948768, 'r': 0.7348571428571429, 'f1': 0.6734747316051324}, 'argument': {'p': 0.6693072723378766, 'r': 0.6747896527256272, 'f1': 0.67203728160642}, 'combined': 0.45260012785852644, 'epoch': 5}
Chinese: {'event': {'p': 0.5569620253164557, 'r': 0.8148148148148148, 'f1': 0.6616541353383458}, 'argument': {'p': 0.6736367607927239, 'r': 0.606829809309148, 'f1': 0.6384904950122339}, 'combined': 0.422459876399072, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5842105263157895, 'r': 0.7390146471371505, 'f1': 0.652557319223986}, 'argument': {'p': 0.6599733374907224, 'r': 0.6585365305150531, 'f1': 0.6592541511440199}, 'combined': 0.4302011215578261, 'epoch': 14}
Test for Korean: {'event': {'p': 0.6217350746268657, 'r': 0.7617142857142857, 'f1': 0.684643040575244}, 'argument': {'p': 0.6870088660626555, 'r': 0.6967372451505287, 'f1': 0.6918388580905483}, 'combined': 0.4736626593912178, 'epoch': 14}
Korean: {'event': {'p': 0.6415094339622641, 'r': 0.5396825396825397, 'f1': 0.5862068965517241}, 'argument': {'p': 0.6945887445887445, 'r': 0.5380617035546612, 'f1': 0.606386999244142}, 'combined': 0.35546824093622115, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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
2023-01-23 11:39:58.664578: step: 2/1532, loss: 0.02929469756782055
2023-01-23 11:39:59.463468: step: 4/1532, loss: 0.05269956216216087
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==================================================
Loss: 0.133
--------------------
Dev Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6619639597961121, 'r': 0.6663803242424909, 'f1': 0.6641648004456004}, 'combined': 0.4438428226939293, 'epoch': 16}
Test Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.67250045002088, 'r': 0.6807604350774312, 'f1': 0.6766052340494917}, 'combined': 0.45673953741282786, 'epoch': 16}
Dev Korean: {'event': {'p': 0.5936842105263158, 'r': 0.7509986684420772, 'f1': 0.6631393298059965}, 'argument': {'p': 0.6551992516620968, 'r': 0.6765954536394513, 'f1': 0.6657254802678785}, 'combined': 0.4414687488196161, 'epoch': 16}
Test Korean: {'event': {'p': 0.6152052238805971, 'r': 0.7537142857142857, 'f1': 0.677452491011813}, 'argument': {'p': 0.6772728314008057, 'r': 0.6985089653590912, 'f1': 0.6877270012464944}, 'combined': 0.46590237013052194, 'epoch': 16}
Dev Russian: {'event': {'p': 0.6296296296296297, 'r': 0.7243675099866844, 'f1': 0.6736842105263158}, 'argument': {'p': 0.6659608851959734, 'r': 0.6664593589124375, 'f1': 0.6662100288118343}, 'combined': 0.4488151773048147, 'epoch': 16}
Test Russian: {'event': {'p': 0.6291624621594349, 'r': 0.7125714285714285, 'f1': 0.6682743837084674}, 'argument': {'p': 0.6797383349642035, 'r': 0.6785516409436655, 'f1': 0.6791444695658424}, 'combined': 0.4538548518481273, 'epoch': 16}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.664119066773934, 'r': 0.6202103681442523, 'f1': 0.6414141414141412}, 'combined': 0.43403964456596017, 'epoch': 16}
Korean: {'event': {'p': 0.6415094339622641, 'r': 0.5396825396825397, 'f1': 0.5862068965517241}, 'argument': {'p': 0.6767399267399268, 'r': 0.4887566137566137, 'f1': 0.5675883256528418}, 'combined': 0.3327241908999417, 'epoch': 16}
Russian: {'event': {'p': 0.5625, 'r': 0.5, 'f1': 0.5294117647058824}, 'argument': {'p': 0.5470573989917976, 'r': 0.5470573989917976, 'f1': 0.5470573989917976}, 'combined': 0.28961862299565755, 'epoch': 16}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6619639597961121, 'r': 0.6663803242424909, 'f1': 0.6641648004456004}, 'combined': 0.4438428226939293, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.67250045002088, 'r': 0.6807604350774312, 'f1': 0.6766052340494917}, 'combined': 0.45673953741282786, 'epoch': 16}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.664119066773934, 'r': 0.6202103681442523, 'f1': 0.6414141414141412}, 'combined': 0.43403964456596017, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5842105263157895, 'r': 0.7390146471371505, 'f1': 0.652557319223986}, 'argument': {'p': 0.6599733374907224, 'r': 0.6585365305150531, 'f1': 0.6592541511440199}, 'combined': 0.4302011215578261, 'epoch': 14}
Test for Korean: {'event': {'p': 0.6217350746268657, 'r': 0.7617142857142857, 'f1': 0.684643040575244}, 'argument': {'p': 0.6870088660626555, 'r': 0.6967372451505287, 'f1': 0.6918388580905483}, 'combined': 0.4736626593912178, 'epoch': 14}
Korean: {'event': {'p': 0.6415094339622641, 'r': 0.5396825396825397, 'f1': 0.5862068965517241}, 'argument': {'p': 0.6945887445887445, 'r': 0.5380617035546612, 'f1': 0.606386999244142}, 'combined': 0.35546824093622115, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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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2023-01-23 12:26:56.202613: step: 3046/1532, loss: 0.5446673631668091
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2023-01-23 12:26:59.532665: step: 3054/1532, loss: 0.010671830736100674
2023-01-23 12:27:00.334136: step: 3056/1532, loss: 0.057938214391469955
2023-01-23 12:27:01.152361: step: 3058/1532, loss: 0.03482688218355179
2023-01-23 12:27:01.975456: step: 3060/1532, loss: 0.023456495255231857
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2023-01-23 12:27:03.573628: step: 3064/1532, loss: 0.017401836812496185
2023-01-23 12:27:04.156467: step: 3066/1532, loss: 0.006134142633527517
==================================================
Loss: 0.143
--------------------
Dev Chinese: {'event': {'p': 0.6043956043956044, 'r': 0.7323568575233023, 'f1': 0.6622516556291391}, 'argument': {'p': 0.6606688919511418, 'r': 0.6509101195441825, 'f1': 0.655753200790791}, 'combined': 0.4342736429078087, 'epoch': 17}
Test Chinese: {'event': {'p': 0.6273562107298212, 'r': 0.7417142857142857, 'f1': 0.6797590992406388}, 'argument': {'p': 0.6793481656811914, 'r': 0.6941871699827601, 'f1': 0.6866875111578281}, 'combined': 0.4667820840444414, 'epoch': 17}
Dev Korean: {'event': {'p': 0.5852631578947368, 'r': 0.7403462050599201, 'f1': 0.6537330981775425}, 'argument': {'p': 0.6515716981031127, 'r': 0.6624391013347092, 'f1': 0.6569604607986795}, 'combined': 0.42947679741806677, 'epoch': 17}
Test Korean: {'event': {'p': 0.621268656716418, 'r': 0.7611428571428571, 'f1': 0.6841294298921418}, 'argument': {'p': 0.6823108186413805, 'r': 0.713837490531387, 'f1': 0.697718199909531}, 'combined': 0.4773295543294788, 'epoch': 17}
Dev Russian: {'event': {'p': 0.625, 'r': 0.7190412782956058, 'f1': 0.6687306501547987}, 'argument': {'p': 0.660881922891825, 'r': 0.6495214713025036, 'f1': 0.6551524527725215}, 'combined': 0.4381205256930794, 'epoch': 17}
Test Russian: {'event': {'p': 0.6326942482341069, 'r': 0.7165714285714285, 'f1': 0.6720257234726686}, 'argument': {'p': 0.6856639895071346, 'r': 0.6854244980682568, 'f1': 0.6855442228714128}, 'combined': 0.46070335234766957, 'epoch': 17}
Chinese: {'event': {'p': 0.5443037974683544, 'r': 0.7962962962962963, 'f1': 0.6466165413533835}, 'argument': {'p': 0.6921097770154374, 'r': 0.6063110443275732, 'f1': 0.6463756507809371}, 'combined': 0.417957187723012, 'epoch': 17}
Korean: {'event': {'p': 0.6226415094339622, 'r': 0.5238095238095238, 'f1': 0.5689655172413793}, 'argument': {'p': 0.768452380952381, 'r': 0.4329309188464118, 'f1': 0.553839553839554}, 'combined': 0.3151156082190566, 'epoch': 17}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.5552401302401303, 'r': 0.5687825724411091, 'f1': 0.5619297703635053}, 'combined': 0.31401957755607657, 'epoch': 17}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6619639597961121, 'r': 0.6663803242424909, 'f1': 0.6641648004456004}, 'combined': 0.4438428226939293, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.67250045002088, 'r': 0.6807604350774312, 'f1': 0.6766052340494917}, 'combined': 0.45673953741282786, 'epoch': 16}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.664119066773934, 'r': 0.6202103681442523, 'f1': 0.6414141414141412}, 'combined': 0.43403964456596017, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5842105263157895, 'r': 0.7390146471371505, 'f1': 0.652557319223986}, 'argument': {'p': 0.6599733374907224, 'r': 0.6585365305150531, 'f1': 0.6592541511440199}, 'combined': 0.4302011215578261, 'epoch': 14}
Test for Korean: {'event': {'p': 0.6217350746268657, 'r': 0.7617142857142857, 'f1': 0.684643040575244}, 'argument': {'p': 0.6870088660626555, 'r': 0.6967372451505287, 'f1': 0.6918388580905483}, 'combined': 0.4736626593912178, 'epoch': 14}
Korean: {'event': {'p': 0.6415094339622641, 'r': 0.5396825396825397, 'f1': 0.5862068965517241}, 'argument': {'p': 0.6945887445887445, 'r': 0.5380617035546612, 'f1': 0.606386999244142}, 'combined': 0.35546824093622115, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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
2023-01-23 12:32:10.597385: step: 2/1532, loss: 0.08523891121149063
2023-01-23 12:32:11.405704: step: 4/1532, loss: 0.009542382322251797
2023-01-23 12:32:12.213917: step: 6/1532, loss: 0.008746151812374592
2023-01-23 12:32:13.012066: step: 8/1532, loss: 0.0036366116255521774
2023-01-23 12:32:13.813240: step: 10/1532, loss: 0.0007123466348275542
2023-01-23 12:32:14.611363: step: 12/1532, loss: 0.017310934141278267
2023-01-23 12:32:15.453617: step: 14/1532, loss: 0.06992185860872269
2023-01-23 12:32:16.277755: step: 16/1532, loss: 0.0828617662191391
2023-01-23 12:32:17.127959: step: 18/1532, loss: 0.12981846928596497
2023-01-23 12:32:17.916527: step: 20/1532, loss: 0.019517812877893448
2023-01-23 12:32:18.714936: step: 22/1532, loss: 0.009394637309014797
2023-01-23 12:32:19.551187: step: 24/1532, loss: 0.017903659492731094
2023-01-23 12:32:20.359629: step: 26/1532, loss: 0.055287301540374756
2023-01-23 12:32:21.204972: step: 28/1532, loss: 0.34335681796073914
2023-01-23 12:32:22.011670: step: 30/1532, loss: 0.002611312549561262
2023-01-23 12:32:22.852063: step: 32/1532, loss: 0.003602690529078245
2023-01-23 12:32:23.704129: step: 34/1532, loss: 0.13018733263015747
2023-01-23 12:32:24.522029: step: 36/1532, loss: 0.024291494861245155
2023-01-23 12:32:25.318042: step: 38/1532, loss: 0.005150898359715939
2023-01-23 12:32:26.119578: step: 40/1532, loss: 0.009191305376589298
2023-01-23 12:32:26.928364: step: 42/1532, loss: 0.19876866042613983
2023-01-23 12:32:27.741555: step: 44/1532, loss: 0.0024132721591740847
2023-01-23 12:32:28.577471: step: 46/1532, loss: 0.013776244595646858
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2023-01-23 12:53:06.134174: step: 3066/1532, loss: 0.00015690922737121582
==================================================
Loss: 0.108
--------------------
Dev Chinese: {'event': {'p': 0.6032967032967033, 'r': 0.7310252996005326, 'f1': 0.6610475617098133}, 'argument': {'p': 0.676198294475149, 'r': 0.6642036462361219, 'f1': 0.6701473030257069}, 'combined': 0.44299924065155094, 'epoch': 18}
Test Chinese: {'event': {'p': 0.626389560173997, 'r': 0.7405714285714285, 'f1': 0.6787117046347211}, 'argument': {'p': 0.6789194213907324, 'r': 0.6891113199035922, 'f1': 0.683977405566349}, 'combined': 0.4642234708635707, 'epoch': 18}
Dev Korean: {'event': {'p': 0.5873684210526315, 'r': 0.7430093209054593, 'f1': 0.656084656084656}, 'argument': {'p': 0.6639110807195848, 'r': 0.6788358403296697, 'f1': 0.6712905154640477}, 'combined': 0.44042340697112126, 'epoch': 18}
Test Korean: {'event': {'p': 0.6226679104477612, 'r': 0.7628571428571429, 'f1': 0.6856702619414484}, 'argument': {'p': 0.6802947726489582, 'r': 0.7053208037723928, 'f1': 0.6925817867697099}, 'combined': 0.4748827351502634, 'epoch': 18}
Dev Russian: {'event': {'p': 0.6261574074074074, 'r': 0.7203728362183754, 'f1': 0.669969040247678}, 'argument': {'p': 0.6695871115483032, 'r': 0.6690866727504345, 'f1': 0.6693367986094066}, 'combined': 0.4484349325667975, 'epoch': 18}
Test Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6849234040873613, 'r': 0.6849234040873613, 'f1': 0.6849234040873613}, 'combined': 0.4599190918121456, 'epoch': 18}
Chinese: {'event': {'p': 0.5316455696202531, 'r': 0.7777777777777778, 'f1': 0.6315789473684209}, 'argument': {'p': 0.6386841386841388, 'r': 0.5859003255697471, 'f1': 0.6111546499477535}, 'combined': 0.3859924104933179, 'epoch': 18}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7338775602214129, 'r': 0.4403265361328478, 'f1': 0.5504081701660598}, 'combined': 0.33214286130710513, 'epoch': 18}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.5397198108405005, 'r': 0.5792115043166347, 'f1': 0.5587687453407535}, 'combined': 0.3122531223963035, 'epoch': 18}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6619639597961121, 'r': 0.6663803242424909, 'f1': 0.6641648004456004}, 'combined': 0.4438428226939293, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.67250045002088, 'r': 0.6807604350774312, 'f1': 0.6766052340494917}, 'combined': 0.45673953741282786, 'epoch': 16}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.664119066773934, 'r': 0.6202103681442523, 'f1': 0.6414141414141412}, 'combined': 0.43403964456596017, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5842105263157895, 'r': 0.7390146471371505, 'f1': 0.652557319223986}, 'argument': {'p': 0.6599733374907224, 'r': 0.6585365305150531, 'f1': 0.6592541511440199}, 'combined': 0.4302011215578261, 'epoch': 14}
Test for Korean: {'event': {'p': 0.6217350746268657, 'r': 0.7617142857142857, 'f1': 0.684643040575244}, 'argument': {'p': 0.6870088660626555, 'r': 0.6967372451505287, 'f1': 0.6918388580905483}, 'combined': 0.4736626593912178, 'epoch': 14}
Korean: {'event': {'p': 0.6415094339622641, 'r': 0.5396825396825397, 'f1': 0.5862068965517241}, 'argument': {'p': 0.6945887445887445, 'r': 0.5380617035546612, 'f1': 0.606386999244142}, 'combined': 0.35546824093622115, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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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2023-01-23 13:19:04.166713: step: 3056/1532, loss: 1.2222509384155273
2023-01-23 13:19:05.008527: step: 3058/1532, loss: 0.02246115915477276
2023-01-23 13:19:05.818072: step: 3060/1532, loss: 0.08080199360847473
2023-01-23 13:19:06.636338: step: 3062/1532, loss: 2.5588910579681396
2023-01-23 13:19:07.458637: step: 3064/1532, loss: 0.022519057616591454
2023-01-23 13:19:08.022591: step: 3066/1532, loss: 0.13557718694210052
==================================================
Loss: 0.109
--------------------
Dev Chinese: {'event': {'p': 0.6054945054945055, 'r': 0.7336884154460719, 'f1': 0.6634557495484648}, 'argument': {'p': 0.6756672758380666, 'r': 0.6512155058852858, 'f1': 0.6632160924924234}, 'combined': 0.4400145297571647, 'epoch': 19}
Test Chinese: {'event': {'p': 0.626389560173997, 'r': 0.7405714285714285, 'f1': 0.6787117046347211}, 'argument': {'p': 0.6895543982813722, 'r': 0.6671891992930723, 'f1': 0.6781874595625226}, 'combined': 0.4602937667415707, 'epoch': 19}
Dev Korean: {'event': {'p': 0.5926315789473684, 'r': 0.7496671105193076, 'f1': 0.6619635508524397}, 'argument': {'p': 0.6686763864725386, 'r': 0.6677065875073862, 'f1': 0.6681911351035454}, 'combined': 0.44231817644126525, 'epoch': 19}
Test Korean: {'event': {'p': 0.6222014925373134, 'r': 0.7622857142857142, 'f1': 0.6851566512583461}, 'argument': {'p': 0.6903048171419265, 'r': 0.6812249224139213, 'f1': 0.6857348141087342}, 'combined': 0.46983576888600476, 'epoch': 19}
Dev Russian: {'event': {'p': 0.6238425925925926, 'r': 0.7177097203728362, 'f1': 0.6674922600619194}, 'argument': {'p': 0.6753639727062303, 'r': 0.6556637521112093, 'f1': 0.6653680732999521}, 'combined': 0.44412803902002984, 'epoch': 19}
Test Russian: {'event': {'p': 0.6326942482341069, 'r': 0.7165714285714285, 'f1': 0.6720257234726686}, 'argument': {'p': 0.694036451955026, 'r': 0.6603524202435216, 'f1': 0.6767755704338686}, 'combined': 0.45481059234944854, 'epoch': 19}
Chinese: {'event': {'p': 0.5443037974683544, 'r': 0.7962962962962963, 'f1': 0.6466165413533835}, 'argument': {'p': 0.6643454749515355, 'r': 0.5435553885967109, 'f1': 0.597910927456382}, 'combined': 0.3866190959492395, 'epoch': 19}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7738397232346456, 'r': 0.453248980751721, 'f1': 0.5716653811282968}, 'combined': 0.34497048861190327, 'epoch': 19}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.5950546445241406, 'r': 0.5660275886936947, 'f1': 0.5801782784110371}, 'combined': 0.3242172732296973, 'epoch': 19}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6619639597961121, 'r': 0.6663803242424909, 'f1': 0.6641648004456004}, 'combined': 0.4438428226939293, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.67250045002088, 'r': 0.6807604350774312, 'f1': 0.6766052340494917}, 'combined': 0.45673953741282786, 'epoch': 16}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.664119066773934, 'r': 0.6202103681442523, 'f1': 0.6414141414141412}, 'combined': 0.43403964456596017, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5926315789473684, 'r': 0.7496671105193076, 'f1': 0.6619635508524397}, 'argument': {'p': 0.6686763864725386, 'r': 0.6677065875073862, 'f1': 0.6681911351035454}, 'combined': 0.44231817644126525, 'epoch': 19}
Test for Korean: {'event': {'p': 0.6222014925373134, 'r': 0.7622857142857142, 'f1': 0.6851566512583461}, 'argument': {'p': 0.6903048171419265, 'r': 0.6812249224139213, 'f1': 0.6857348141087342}, 'combined': 0.46983576888600476, 'epoch': 19}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7738397232346456, 'r': 0.453248980751721, 'f1': 0.5716653811282968}, 'combined': 0.34497048861190327, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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
2023-01-23 13:24:18.005356: step: 2/1532, loss: 0.0020560086704790592
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2023-01-23 13:45:13.232307: step: 3066/1532, loss: 0.002526757773011923
==================================================
Loss: 0.104
--------------------
Dev Chinese: {'event': {'p': 0.6032967032967033, 'r': 0.7310252996005326, 'f1': 0.6610475617098133}, 'argument': {'p': 0.6545342095519472, 'r': 0.6516273218707395, 'f1': 0.6530775310440422}, 'combined': 0.43171530950412895, 'epoch': 20}
Test Chinese: {'event': {'p': 0.6259062348960851, 'r': 0.74, 'f1': 0.6781880073317623}, 'argument': {'p': 0.688052686843289, 'r': 0.6711217936605424, 'f1': 0.6794817883220577}, 'combined': 0.4608164000403587, 'epoch': 20}
Dev Korean: {'event': {'p': 0.5863157894736842, 'r': 0.7416777629826897, 'f1': 0.6549088771310994}, 'argument': {'p': 0.6498171806187905, 'r': 0.6611347512536605, 'f1': 0.6554271131133411}, 'combined': 0.42924503469033626, 'epoch': 20}
Test Korean: {'event': {'p': 0.6222014925373134, 'r': 0.7622857142857142, 'f1': 0.6851566512583461}, 'argument': {'p': 0.691518967172635, 'r': 0.6903528306344011, 'f1': 0.6909354068627845}, 'combined': 0.47339898960192833, 'epoch': 20}
Dev Russian: {'event': {'p': 0.6226851851851852, 'r': 0.7163781624500666, 'f1': 0.6662538699690402}, 'argument': {'p': 0.6615537502751747, 'r': 0.6551212904744438, 'f1': 0.6583218078649615}, 'combined': 0.43860945217504554, 'epoch': 20}
Test Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6977167501532489, 'r': 0.6689901009843433, 'f1': 0.6830515245533052}, 'combined': 0.4586621437648935, 'epoch': 20}
Chinese: {'event': {'p': 0.5316455696202531, 'r': 0.7777777777777778, 'f1': 0.6315789473684209}, 'argument': {'p': 0.6570578953756524, 'r': 0.5810346678115272, 'f1': 0.6167122351332878}, 'combined': 0.389502464294708, 'epoch': 20}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7267080745341615, 'r': 0.4775510204081633, 'f1': 0.5763546798029556}, 'combined': 0.34780023781212843, 'epoch': 20}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.5303238105563687, 'r': 0.5561932647298501, 'f1': 0.542950567950568}, 'combined': 0.30341355267825865, 'epoch': 20}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6619639597961121, 'r': 0.6663803242424909, 'f1': 0.6641648004456004}, 'combined': 0.4438428226939293, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.67250045002088, 'r': 0.6807604350774312, 'f1': 0.6766052340494917}, 'combined': 0.45673953741282786, 'epoch': 16}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.664119066773934, 'r': 0.6202103681442523, 'f1': 0.6414141414141412}, 'combined': 0.43403964456596017, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5926315789473684, 'r': 0.7496671105193076, 'f1': 0.6619635508524397}, 'argument': {'p': 0.6686763864725386, 'r': 0.6677065875073862, 'f1': 0.6681911351035454}, 'combined': 0.44231817644126525, 'epoch': 19}
Test for Korean: {'event': {'p': 0.6222014925373134, 'r': 0.7622857142857142, 'f1': 0.6851566512583461}, 'argument': {'p': 0.6903048171419265, 'r': 0.6812249224139213, 'f1': 0.6857348141087342}, 'combined': 0.46983576888600476, 'epoch': 19}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7738397232346456, 'r': 0.453248980751721, 'f1': 0.5716653811282968}, 'combined': 0.34497048861190327, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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
2023-01-23 13:50:18.811006: step: 2/1532, loss: 0.013749693520367146
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2023-01-23 14:11:13.411842: step: 3066/1532, loss: 0.004628264810889959
==================================================
Loss: 0.105
--------------------
Dev Chinese: {'event': {'p': 0.6065934065934065, 'r': 0.7350199733688415, 'f1': 0.6646598434677904}, 'argument': {'p': 0.6540566031428062, 'r': 0.6598618392653756, 'f1': 0.6569463966765742}, 'combined': 0.4366458891817807, 'epoch': 21}
Test Chinese: {'event': {'p': 0.6220396326727888, 'r': 0.7354285714285714, 'f1': 0.6739984289080913}, 'argument': {'p': 0.6790023182217046, 'r': 0.6790023182217046, 'f1': 0.6790023182217046}, 'combined': 0.4576464957063808, 'epoch': 21}
Dev Korean: {'event': {'p': 0.588421052631579, 'r': 0.7443408788282291, 'f1': 0.6572604350382129}, 'argument': {'p': 0.6492301390691317, 'r': 0.6765364103280219, 'f1': 0.6626020666493907}, 'combined': 0.4355021225831974, 'epoch': 21}
Test Korean: {'event': {'p': 0.6175373134328358, 'r': 0.7565714285714286, 'f1': 0.6800205444273241}, 'argument': {'p': 0.6819968137206573, 'r': 0.6971727374244885, 'f1': 0.6895012801603978}, 'combined': 0.4688750359180106, 'epoch': 21}
Dev Russian: {'event': {'p': 0.6261574074074074, 'r': 0.7203728362183754, 'f1': 0.669969040247678}, 'argument': {'p': 0.654150255852513, 'r': 0.6703566811101203, 'f1': 0.6621543190498185}, 'combined': 0.4436228936296617, 'epoch': 21}
Test Russian: {'event': {'p': 0.6291624621594349, 'r': 0.7125714285714285, 'f1': 0.6682743837084674}, 'argument': {'p': 0.6882282640566222, 'r': 0.6776696714420855, 'f1': 0.6829081580098035}, 'combined': 0.4563700284234861, 'epoch': 21}
Chinese: {'event': {'p': 0.5316455696202531, 'r': 0.7777777777777778, 'f1': 0.6315789473684209}, 'argument': {'p': 0.6607551253777668, 'r': 0.5788433329755643, 'f1': 0.6170928924232888}, 'combined': 0.38974287942523494, 'epoch': 21}
Korean: {'event': {'p': 0.6415094339622641, 'r': 0.5396825396825397, 'f1': 0.5862068965517241}, 'argument': {'p': 0.7252089571006267, 'r': 0.49354498469348207, 'f1': 0.5873593206269537}, 'combined': 0.3443140845054556, 'epoch': 21}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.5205589607754804, 'r': 0.5459520808133087, 'f1': 0.5329532217463251}, 'combined': 0.2978268003876523, 'epoch': 21}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6619639597961121, 'r': 0.6663803242424909, 'f1': 0.6641648004456004}, 'combined': 0.4438428226939293, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.67250045002088, 'r': 0.6807604350774312, 'f1': 0.6766052340494917}, 'combined': 0.45673953741282786, 'epoch': 16}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.664119066773934, 'r': 0.6202103681442523, 'f1': 0.6414141414141412}, 'combined': 0.43403964456596017, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5926315789473684, 'r': 0.7496671105193076, 'f1': 0.6619635508524397}, 'argument': {'p': 0.6686763864725386, 'r': 0.6677065875073862, 'f1': 0.6681911351035454}, 'combined': 0.44231817644126525, 'epoch': 19}
Test for Korean: {'event': {'p': 0.6222014925373134, 'r': 0.7622857142857142, 'f1': 0.6851566512583461}, 'argument': {'p': 0.6903048171419265, 'r': 0.6812249224139213, 'f1': 0.6857348141087342}, 'combined': 0.46983576888600476, 'epoch': 19}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7738397232346456, 'r': 0.453248980751721, 'f1': 0.5716653811282968}, 'combined': 0.34497048861190327, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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
2023-01-23 14:16:20.598716: step: 2/1532, loss: 0.07512961328029633
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2023-01-23 14:37:11.385552: step: 3058/1532, loss: 0.08682984113693237
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2023-01-23 14:37:12.969278: step: 3062/1532, loss: 0.04263974353671074
2023-01-23 14:37:13.785395: step: 3064/1532, loss: 0.005598272196948528
2023-01-23 14:37:14.378802: step: 3066/1532, loss: 0.001123058726079762
==================================================
Loss: 0.082
--------------------
Dev Chinese: {'event': {'p': 0.6065934065934065, 'r': 0.7350199733688415, 'f1': 0.6646598434677904}, 'argument': {'p': 0.6510364783433193, 'r': 0.6548859179787565, 'f1': 0.652955524705422}, 'combined': 0.4339933168421347, 'epoch': 22}
Test Chinese: {'event': {'p': 0.6210729821169647, 'r': 0.7342857142857143, 'f1': 0.6729510343021734}, 'argument': {'p': 0.684868293827736, 'r': 0.6771600217621442, 'f1': 0.6809923456719625}, 'combined': 0.4582745033718104, 'epoch': 22}
Dev Korean: {'event': {'p': 0.5852631578947368, 'r': 0.7403462050599201, 'f1': 0.6537330981775425}, 'argument': {'p': 0.6467352468811016, 'r': 0.6711226528548633, 'f1': 0.6587033012717839}, 'combined': 0.4306161499201785, 'epoch': 22}
Test Korean: {'event': {'p': 0.6152052238805971, 'r': 0.7537142857142857, 'f1': 0.677452491011813}, 'argument': {'p': 0.6862877632736551, 'r': 0.6948547832605395, 'f1': 0.690544703297314}, 'combined': 0.46781122940377873, 'epoch': 22}
Dev Russian: {'event': {'p': 0.6238425925925926, 'r': 0.7177097203728362, 'f1': 0.6674922600619194}, 'argument': {'p': 0.6501931075465641, 'r': 0.6560331653987788, 'f1': 0.6531000812017649}, 'combined': 0.43593924924798916, 'epoch': 22}
Test Russian: {'event': {'p': 0.6291624621594349, 'r': 0.7125714285714285, 'f1': 0.6682743837084674}, 'argument': {'p': 0.6890944681469539, 'r': 0.6756205539653095, 'f1': 0.6822909966701856}, 'combined': 0.4559575953096042, 'epoch': 22}
Chinese: {'event': {'p': 0.5316455696202531, 'r': 0.7777777777777778, 'f1': 0.6315789473684209}, 'argument': {'p': 0.6888077200577201, 'r': 0.5920330817025031, 'f1': 0.6367644700978035}, 'combined': 0.40216703374598106, 'epoch': 22}
Korean: {'event': {'p': 0.6415094339622641, 'r': 0.5396825396825397, 'f1': 0.5862068965517241}, 'argument': {'p': 0.6777284988069302, 'r': 0.5082963741051976, 'f1': 0.5809101418345116}, 'combined': 0.3405335314202309, 'epoch': 22}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.5548212604141957, 'r': 0.5548212604141957, 'f1': 0.5548212604141957}, 'combined': 0.3100471749373447, 'epoch': 22}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6619639597961121, 'r': 0.6663803242424909, 'f1': 0.6641648004456004}, 'combined': 0.4438428226939293, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.67250045002088, 'r': 0.6807604350774312, 'f1': 0.6766052340494917}, 'combined': 0.45673953741282786, 'epoch': 16}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.664119066773934, 'r': 0.6202103681442523, 'f1': 0.6414141414141412}, 'combined': 0.43403964456596017, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5926315789473684, 'r': 0.7496671105193076, 'f1': 0.6619635508524397}, 'argument': {'p': 0.6686763864725386, 'r': 0.6677065875073862, 'f1': 0.6681911351035454}, 'combined': 0.44231817644126525, 'epoch': 19}
Test for Korean: {'event': {'p': 0.6222014925373134, 'r': 0.7622857142857142, 'f1': 0.6851566512583461}, 'argument': {'p': 0.6903048171419265, 'r': 0.6812249224139213, 'f1': 0.6857348141087342}, 'combined': 0.46983576888600476, 'epoch': 19}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7738397232346456, 'r': 0.453248980751721, 'f1': 0.5716653811282968}, 'combined': 0.34497048861190327, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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
2023-01-23 14:42:19.501710: step: 2/1532, loss: 0.00025755399838089943
2023-01-23 14:42:20.353204: step: 4/1532, loss: 0.00018884673772845417
2023-01-23 14:42:21.163556: step: 6/1532, loss: 0.0035663454327732325
2023-01-23 14:42:21.996485: step: 8/1532, loss: 4.2804033000720665e-05
2023-01-23 14:42:22.853751: step: 10/1532, loss: 0.08732438832521439
2023-01-23 14:42:23.673622: step: 12/1532, loss: 0.0008254846325144172
2023-01-23 14:42:24.500699: step: 14/1532, loss: 0.1483704149723053
2023-01-23 14:42:25.309368: step: 16/1532, loss: 0.03844952955842018
2023-01-23 14:42:26.183849: step: 18/1532, loss: 0.7460244297981262
2023-01-23 14:42:27.004065: step: 20/1532, loss: -3.286550781922415e-05
2023-01-23 14:42:27.839745: step: 22/1532, loss: 0.4398888051509857
2023-01-23 14:42:28.670273: step: 24/1532, loss: 0.06562767922878265
2023-01-23 14:42:29.468806: step: 26/1532, loss: 0.0001911755680339411
2023-01-23 14:42:30.316815: step: 28/1532, loss: 0.1183893159031868
2023-01-23 14:42:31.151665: step: 30/1532, loss: 0.062176235020160675
2023-01-23 14:42:31.961474: step: 32/1532, loss: 0.0016450032126158476
2023-01-23 14:42:32.781599: step: 34/1532, loss: 0.0011887886794283986
2023-01-23 14:42:33.632559: step: 36/1532, loss: 0.0007875513983890414
2023-01-23 14:42:34.439334: step: 38/1532, loss: 0.033164262771606445
2023-01-23 14:42:35.269806: step: 40/1532, loss: 2.3977645469130948e-05
2023-01-23 14:42:36.077874: step: 42/1532, loss: 0.012396175414323807
2023-01-23 14:42:36.886261: step: 44/1532, loss: 0.0017017372883856297
2023-01-23 14:42:37.657672: step: 46/1532, loss: 0.0010843385243788362
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==================================================
Loss: 0.086
--------------------
Dev Chinese: {'event': {'p': 0.6032967032967033, 'r': 0.7310252996005326, 'f1': 0.6610475617098133}, 'argument': {'p': 0.6669207500483875, 'r': 0.6580612862101063, 'f1': 0.6624613988222094}, 'combined': 0.4379184924182937, 'epoch': 23}
Test Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.6953091678778984, 'r': 0.6793769810626388, 'f1': 0.6872507499142555}, 'combined': 0.46392574843648876, 'epoch': 23}
Dev Korean: {'event': {'p': 0.5852631578947368, 'r': 0.7403462050599201, 'f1': 0.6537330981775425}, 'argument': {'p': 0.6657237886719443, 'r': 0.6676534228420078, 'f1': 0.6666872094949137}, 'combined': 0.43583549497845026, 'epoch': 23}
Test Korean: {'event': {'p': 0.6203358208955224, 'r': 0.76, 'f1': 0.6831022085259374}, 'argument': {'p': 0.6980252996467213, 'r': 0.6954339131141145, 'f1': 0.6967271968071416}, 'combined': 0.47593588687904387, 'epoch': 23}
Dev Russian: {'event': {'p': 0.6168981481481481, 'r': 0.7097203728362184, 'f1': 0.6600619195046439}, 'argument': {'p': 0.6690162189628874, 'r': 0.6560256127694333, 'f1': 0.6624572364240355}, 'combined': 0.43726279506379057, 'epoch': 23}
Test Russian: {'event': {'p': 0.6326942482341069, 'r': 0.7165714285714285, 'f1': 0.6720257234726686}, 'argument': {'p': 0.6980405874116166, 'r': 0.6697185488877454, 'f1': 0.6835863374759243}, 'combined': 0.4593876029982899, 'epoch': 23}
Chinese: {'event': {'p': 0.5443037974683544, 'r': 0.7962962962962963, 'f1': 0.6466165413533835}, 'argument': {'p': 0.702041847041847, 'r': 0.5801998735883033, 'f1': 0.6353319882731647}, 'combined': 0.4108161728683622, 'epoch': 23}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7429038281979459, 'r': 0.4775810324129652, 'f1': 0.581402995981001}, 'combined': 0.3508466355057765, 'epoch': 23}
Russian: {'event': {'p': 0.5625, 'r': 0.5, 'f1': 0.5294117647058824}, 'argument': {'p': 0.5492473474801061, 'r': 0.5492473474801061, 'f1': 0.5492473474801061}, 'combined': 0.2907780074894679, 'epoch': 23}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6619639597961121, 'r': 0.6663803242424909, 'f1': 0.6641648004456004}, 'combined': 0.4438428226939293, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.67250045002088, 'r': 0.6807604350774312, 'f1': 0.6766052340494917}, 'combined': 0.45673953741282786, 'epoch': 16}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.664119066773934, 'r': 0.6202103681442523, 'f1': 0.6414141414141412}, 'combined': 0.43403964456596017, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5926315789473684, 'r': 0.7496671105193076, 'f1': 0.6619635508524397}, 'argument': {'p': 0.6686763864725386, 'r': 0.6677065875073862, 'f1': 0.6681911351035454}, 'combined': 0.44231817644126525, 'epoch': 19}
Test for Korean: {'event': {'p': 0.6222014925373134, 'r': 0.7622857142857142, 'f1': 0.6851566512583461}, 'argument': {'p': 0.6903048171419265, 'r': 0.6812249224139213, 'f1': 0.6857348141087342}, 'combined': 0.46983576888600476, 'epoch': 19}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7738397232346456, 'r': 0.453248980751721, 'f1': 0.5716653811282968}, 'combined': 0.34497048861190327, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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
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2023-01-23 15:29:11.644099: step: 3066/1532, loss: 0.0008060553809627891
==================================================
Loss: 0.081
--------------------
Dev Chinese: {'event': {'p': 0.6076923076923076, 'r': 0.7363515312916112, 'f1': 0.6658639373871162}, 'argument': {'p': 0.6708653734670701, 'r': 0.659452722882941, 'f1': 0.665110094246726}, 'combined': 0.4428728261510409, 'epoch': 24}
Test Chinese: {'event': {'p': 0.6259062348960851, 'r': 0.74, 'f1': 0.6781880073317623}, 'argument': {'p': 0.6821144646513976, 'r': 0.6807152554931383, 'f1': 0.6814141417924126}, 'combined': 0.46212689898987924, 'epoch': 24}
Dev Korean: {'event': {'p': 0.5894736842105263, 'r': 0.7456724367509987, 'f1': 0.6584362139917695}, 'argument': {'p': 0.6643184855174847, 'r': 0.6715445749176023, 'f1': 0.6679119861603849}, 'combined': 0.439777439447167, 'epoch': 24}
Test Korean: {'event': {'p': 0.6189365671641791, 'r': 0.7582857142857143, 'f1': 0.6815613764766307}, 'argument': {'p': 0.6863002468663677, 'r': 0.6981050740468684, 'f1': 0.6921523305631048}, 'combined': 0.47174429515009764, 'epoch': 24}
Dev Russian: {'event': {'p': 0.6284722222222222, 'r': 0.7230359520639148, 'f1': 0.6724458204334366}, 'argument': {'p': 0.6697511266992714, 'r': 0.66072753367488, 'f1': 0.6652087302107308}, 'combined': 0.4473168303460394, 'epoch': 24}
Test Russian: {'event': {'p': 0.6311806256306761, 'r': 0.7148571428571429, 'f1': 0.6704180064308681}, 'argument': {'p': 0.6883316826523465, 'r': 0.6782374331162436, 'f1': 0.6832472769767576}, 'combined': 0.458061277330077, 'epoch': 24}
Chinese: {'event': {'p': 0.5443037974683544, 'r': 0.7962962962962963, 'f1': 0.6466165413533835}, 'argument': {'p': 0.655885780885781, 'r': 0.5637365389431506, 'f1': 0.6063299663299664}, 'combined': 0.39206298574719634, 'epoch': 24}
Korean: {'event': {'p': 0.6226415094339622, 'r': 0.5238095238095238, 'f1': 0.5689655172413793}, 'argument': {'p': 0.7551596654575442, 'r': 0.5424386329342923, 'f1': 0.6313629989890943}, 'combined': 0.35922377528689853, 'epoch': 24}
Russian: {'event': {'p': 0.5625, 'r': 0.5, 'f1': 0.5294117647058824}, 'argument': {'p': 0.5323247430575017, 'r': 0.5323247430575017, 'f1': 0.5323247430575017}, 'combined': 0.28181898161867736, 'epoch': 24}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6619639597961121, 'r': 0.6663803242424909, 'f1': 0.6641648004456004}, 'combined': 0.4438428226939293, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.67250045002088, 'r': 0.6807604350774312, 'f1': 0.6766052340494917}, 'combined': 0.45673953741282786, 'epoch': 16}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.664119066773934, 'r': 0.6202103681442523, 'f1': 0.6414141414141412}, 'combined': 0.43403964456596017, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5894736842105263, 'r': 0.7456724367509987, 'f1': 0.6584362139917695}, 'argument': {'p': 0.6643184855174847, 'r': 0.6715445749176023, 'f1': 0.6679119861603849}, 'combined': 0.439777439447167, 'epoch': 24}
Test for Korean: {'event': {'p': 0.6189365671641791, 'r': 0.7582857142857143, 'f1': 0.6815613764766307}, 'argument': {'p': 0.6863002468663677, 'r': 0.6981050740468684, 'f1': 0.6921523305631048}, 'combined': 0.47174429515009764, 'epoch': 24}
Korean: {'event': {'p': 0.6226415094339622, 'r': 0.5238095238095238, 'f1': 0.5689655172413793}, 'argument': {'p': 0.7551596654575442, 'r': 0.5424386329342923, 'f1': 0.6313629989890943}, 'combined': 0.35922377528689853, 'epoch': 24}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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
2023-01-23 15:34:25.600308: step: 2/1532, loss: 0.02327515184879303
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2023-01-23 15:55:18.607738: step: 3066/1532, loss: 1.191580057144165
==================================================
Loss: 0.076
--------------------
Dev Chinese: {'event': {'p': 0.6021978021978022, 'r': 0.729693741677763, 'f1': 0.6598434677904876}, 'argument': {'p': 0.6796823471939794, 'r': 0.6625390238968019, 'f1': 0.6710012052688873}, 'combined': 0.4427557621762194, 'epoch': 25}
Test Chinese: {'event': {'p': 0.625422909618173, 'r': 0.7394285714285714, 'f1': 0.6776643100288033}, 'argument': {'p': 0.6877420926853264, 'r': 0.678816478094465, 'f1': 0.683250136768853}, 'combined': 0.4630142325105503, 'epoch': 25}
Dev Korean: {'event': {'p': 0.5821052631578948, 'r': 0.7363515312916112, 'f1': 0.6502057613168725}, 'argument': {'p': 0.6758296223890353, 'r': 0.6699528430639133, 'f1': 0.672878401330568}, 'combined': 0.437509413210822, 'epoch': 25}
Test Korean: {'event': {'p': 0.621268656716418, 'r': 0.7611428571428571, 'f1': 0.6841294298921418}, 'argument': {'p': 0.6879208268481474, 'r': 0.6918624021957774, 'f1': 0.6898859846462347}, 'combined': 0.4719713053666074, 'epoch': 25}
Dev Russian: {'event': {'p': 0.6261574074074074, 'r': 0.7203728362183754, 'f1': 0.669969040247678}, 'argument': {'p': 0.6798261790648596, 'r': 0.663494018606845, 'f1': 0.6715608151856516}, 'combined': 0.44992495481787925, 'epoch': 25}
Test Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6902353619883764, 'r': 0.6723823676552273, 'f1': 0.6811919100076506}, 'combined': 0.4574134315324684, 'epoch': 25}
Chinese: {'event': {'p': 0.5443037974683544, 'r': 0.7962962962962963, 'f1': 0.6466165413533835}, 'argument': {'p': 0.6617276662731207, 'r': 0.601570605702837, 'f1': 0.6302168250220198}, 'combined': 0.4075086236984489, 'epoch': 25}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7129263470093921, 'r': 0.5194177671068427, 'f1': 0.6009792346690742}, 'combined': 0.36265988298995866, 'epoch': 25}
Russian: {'event': {'p': 0.5625, 'r': 0.5, 'f1': 0.5294117647058824}, 'argument': {'p': 0.5602413146147377, 'r': 0.5602413146147377, 'f1': 0.5602413146147377}, 'combined': 0.2965983430313317, 'epoch': 25}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6619639597961121, 'r': 0.6663803242424909, 'f1': 0.6641648004456004}, 'combined': 0.4438428226939293, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.67250045002088, 'r': 0.6807604350774312, 'f1': 0.6766052340494917}, 'combined': 0.45673953741282786, 'epoch': 16}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.664119066773934, 'r': 0.6202103681442523, 'f1': 0.6414141414141412}, 'combined': 0.43403964456596017, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5821052631578948, 'r': 0.7363515312916112, 'f1': 0.6502057613168725}, 'argument': {'p': 0.6758296223890353, 'r': 0.6699528430639133, 'f1': 0.672878401330568}, 'combined': 0.437509413210822, 'epoch': 25}
Test for Korean: {'event': {'p': 0.621268656716418, 'r': 0.7611428571428571, 'f1': 0.6841294298921418}, 'argument': {'p': 0.6879208268481474, 'r': 0.6918624021957774, 'f1': 0.6898859846462347}, 'combined': 0.4719713053666074, 'epoch': 25}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7129263470093921, 'r': 0.5194177671068427, 'f1': 0.6009792346690742}, 'combined': 0.36265988298995866, 'epoch': 25}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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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2023-01-23 16:21:22.350298: step: 3062/1532, loss: 0.009898435324430466
2023-01-23 16:21:23.164856: step: 3064/1532, loss: 0.0771232545375824
2023-01-23 16:21:23.733062: step: 3066/1532, loss: 0.006207536440342665
==================================================
Loss: 0.064
--------------------
Dev Chinese: {'event': {'p': 0.6043956043956044, 'r': 0.7323568575233023, 'f1': 0.6622516556291391}, 'argument': {'p': 0.6560104216645677, 'r': 0.6535861407271525, 'f1': 0.6547960373223526}, 'combined': 0.43363975981612757, 'epoch': 26}
Test Chinese: {'event': {'p': 0.6273562107298212, 'r': 0.7417142857142857, 'f1': 0.6797590992406388}, 'argument': {'p': 0.6697681350981093, 'r': 0.6891982687101704, 'f1': 0.6793442985099409}, 'combined': 0.4617904684293811, 'epoch': 26}
Dev Korean: {'event': {'p': 0.5863157894736842, 'r': 0.7416777629826897, 'f1': 0.6549088771310994}, 'argument': {'p': 0.6526304161660069, 'r': 0.672521909256698, 'f1': 0.6624268698754323}, 'combined': 0.43382923753158825, 'epoch': 26}
Test Korean: {'event': {'p': 0.621268656716418, 'r': 0.7611428571428571, 'f1': 0.6841294298921418}, 'argument': {'p': 0.6731062944319901, 'r': 0.7044452514602001, 'f1': 0.6884192961846467}, 'combined': 0.4709679006255518, 'epoch': 26}
Dev Russian: {'event': {'p': 0.6238425925925926, 'r': 0.7177097203728362, 'f1': 0.6674922600619194}, 'argument': {'p': 0.6580455245731855, 'r': 0.6565645518997275, 'f1': 0.6573042040423108}, 'combined': 0.43874546870440306, 'epoch': 26}
Test Russian: {'event': {'p': 0.6311806256306761, 'r': 0.7148571428571429, 'f1': 0.6704180064308681}, 'argument': {'p': 0.6789491611237447, 'r': 0.6855799865550195, 'f1': 0.6822484628438878}, 'combined': 0.4573916543503235, 'epoch': 26}
Chinese: {'event': {'p': 0.5443037974683544, 'r': 0.7962962962962963, 'f1': 0.6466165413533835}, 'argument': {'p': 0.6443722943722944, 'r': 0.5591660405709993, 'f1': 0.5987530168946099}, 'combined': 0.3871636049092967, 'epoch': 26}
Korean: {'event': {'p': 0.6415094339622641, 'r': 0.5396825396825397, 'f1': 0.5862068965517241}, 'argument': {'p': 0.653429250488074, 'r': 0.5061775884062545, 'f1': 0.5704541075689535}, 'combined': 0.33440413202317965, 'epoch': 26}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.5522714343484192, 'r': 0.5792115043166347, 'f1': 0.5654207542138577}, 'combined': 0.31597042147244997, 'epoch': 26}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6619639597961121, 'r': 0.6663803242424909, 'f1': 0.6641648004456004}, 'combined': 0.4438428226939293, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.67250045002088, 'r': 0.6807604350774312, 'f1': 0.6766052340494917}, 'combined': 0.45673953741282786, 'epoch': 16}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.664119066773934, 'r': 0.6202103681442523, 'f1': 0.6414141414141412}, 'combined': 0.43403964456596017, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5821052631578948, 'r': 0.7363515312916112, 'f1': 0.6502057613168725}, 'argument': {'p': 0.6758296223890353, 'r': 0.6699528430639133, 'f1': 0.672878401330568}, 'combined': 0.437509413210822, 'epoch': 25}
Test for Korean: {'event': {'p': 0.621268656716418, 'r': 0.7611428571428571, 'f1': 0.6841294298921418}, 'argument': {'p': 0.6879208268481474, 'r': 0.6918624021957774, 'f1': 0.6898859846462347}, 'combined': 0.4719713053666074, 'epoch': 25}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7129263470093921, 'r': 0.5194177671068427, 'f1': 0.6009792346690742}, 'combined': 0.36265988298995866, 'epoch': 25}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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
2023-01-23 16:26:31.581885: step: 2/1532, loss: 0.05958016216754913
2023-01-23 16:26:32.403002: step: 4/1532, loss: 0.3882599174976349
2023-01-23 16:26:33.241736: step: 6/1532, loss: 0.005846207495778799
2023-01-23 16:26:34.078395: step: 8/1532, loss: 0.0008743959479033947
2023-01-23 16:26:34.864729: step: 10/1532, loss: 0.00030329078435897827
2023-01-23 16:26:35.674805: step: 12/1532, loss: 0.09928272664546967
2023-01-23 16:26:36.463026: step: 14/1532, loss: 0.005956043489277363
2023-01-23 16:26:37.282716: step: 16/1532, loss: 0.00029772851848974824
2023-01-23 16:26:38.091915: step: 18/1532, loss: 0.12212856113910675
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2023-01-23 16:47:23.961745: step: 3066/1532, loss: 1.5136241927393712e-05
==================================================
Loss: 0.069
--------------------
Dev Chinese: {'event': {'p': 0.6087912087912087, 'r': 0.7376830892143809, 'f1': 0.6670680313064418}, 'argument': {'p': 0.6869768477329423, 'r': 0.6355045468271358, 'f1': 0.6602390205186736}, 'combined': 0.440424343609085, 'epoch': 27}
Test Chinese: {'event': {'p': 0.624939584340261, 'r': 0.7388571428571429, 'f1': 0.6771406127258445}, 'argument': {'p': 0.6978138072801287, 'r': 0.6722956673693379, 'f1': 0.6848171010348765}, 'combined': 0.4637174713998928, 'epoch': 27}
Dev Korean: {'event': {'p': 0.5915789473684211, 'r': 0.748335552596538, 'f1': 0.660787771898883}, 'argument': {'p': 0.6854896564300665, 'r': 0.6471579908199612, 'f1': 0.6657725464020071}, 'combined': 0.439934357528428, 'epoch': 27}
Test Korean: {'event': {'p': 0.6222014925373134, 'r': 0.7622857142857142, 'f1': 0.6851566512583461}, 'argument': {'p': 0.69978938204972, 'r': 0.6884567604780646, 'f1': 0.6940768156656406}, 'combined': 0.4755513467375267, 'epoch': 27}
Dev Russian: {'event': {'p': 0.625, 'r': 0.7190412782956058, 'f1': 0.6687306501547987}, 'argument': {'p': 0.6910701069696201, 'r': 0.6387477075663731, 'f1': 0.6638795807507143}, 'combined': 0.44395662365992034, 'epoch': 27}
Test Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.7036154692270645, 'r': 0.6723819269020602, 'f1': 0.6876442155019818}, 'combined': 0.4617460889731957, 'epoch': 27}
Chinese: {'event': {'p': 0.5316455696202531, 'r': 0.7777777777777778, 'f1': 0.6315789473684209}, 'argument': {'p': 0.6886363636363636, 'r': 0.5122088655146506, 'f1': 0.5874623007324428}, 'combined': 0.3710288215152269, 'epoch': 27}
Korean: {'event': {'p': 0.6226415094339622, 'r': 0.5238095238095238, 'f1': 0.5689655172413793}, 'argument': {'p': 0.6377912867274569, 'r': 0.42823129251700676, 'f1': 0.5124135124135123}, 'combined': 0.291545619131826, 'epoch': 27}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.571558693972487, 'r': 0.5157968701702932, 'f1': 0.5422479917174877}, 'combined': 0.30302093654800794, 'epoch': 27}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6619639597961121, 'r': 0.6663803242424909, 'f1': 0.6641648004456004}, 'combined': 0.4438428226939293, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.67250045002088, 'r': 0.6807604350774312, 'f1': 0.6766052340494917}, 'combined': 0.45673953741282786, 'epoch': 16}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.664119066773934, 'r': 0.6202103681442523, 'f1': 0.6414141414141412}, 'combined': 0.43403964456596017, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5821052631578948, 'r': 0.7363515312916112, 'f1': 0.6502057613168725}, 'argument': {'p': 0.6758296223890353, 'r': 0.6699528430639133, 'f1': 0.672878401330568}, 'combined': 0.437509413210822, 'epoch': 25}
Test for Korean: {'event': {'p': 0.621268656716418, 'r': 0.7611428571428571, 'f1': 0.6841294298921418}, 'argument': {'p': 0.6879208268481474, 'r': 0.6918624021957774, 'f1': 0.6898859846462347}, 'combined': 0.4719713053666074, 'epoch': 25}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7129263470093921, 'r': 0.5194177671068427, 'f1': 0.6009792346690742}, 'combined': 0.36265988298995866, 'epoch': 25}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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
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2023-01-23 17:13:19.048736: step: 3058/1532, loss: 0.0020663754548877478
2023-01-23 17:13:19.855053: step: 3060/1532, loss: 3.461336746113375e-05
2023-01-23 17:13:20.692927: step: 3062/1532, loss: 0.00410714466124773
2023-01-23 17:13:21.539219: step: 3064/1532, loss: 0.00021951676171738654
2023-01-23 17:13:22.092464: step: 3066/1532, loss: 1.8927368728327565e-05
==================================================
Loss: 0.073
--------------------
Dev Chinese: {'event': {'p': 0.6087912087912087, 'r': 0.7376830892143809, 'f1': 0.6670680313064418}, 'argument': {'p': 0.671128160273757, 'r': 0.646308331861266, 'f1': 0.6584844496431286}, 'combined': 0.43925392546934755, 'epoch': 28}
Test Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.6942273302830863, 'r': 0.6747984425413752, 'f1': 0.6843750213382588}, 'combined': 0.4619844998717023, 'epoch': 28}
Dev Korean: {'event': {'p': 0.588421052631579, 'r': 0.7443408788282291, 'f1': 0.6572604350382129}, 'argument': {'p': 0.6636380211334715, 'r': 0.6540131042207308, 'f1': 0.658790409583921}, 'combined': 0.4329968712021303, 'epoch': 28}
Test Korean: {'event': {'p': 0.6175373134328358, 'r': 0.7565714285714286, 'f1': 0.6800205444273241}, 'argument': {'p': 0.6972236274482209, 'r': 0.6941645970206721, 'f1': 0.6956907495255376}, 'combined': 0.4730840022454092, 'epoch': 28}
Dev Russian: {'event': {'p': 0.6296296296296297, 'r': 0.7243675099866844, 'f1': 0.6736842105263158}, 'argument': {'p': 0.66918241083041, 'r': 0.647612063254917, 'f1': 0.6582205656244261}, 'combined': 0.4434328021048765, 'epoch': 28}
Test Russian: {'event': {'p': 0.6301715438950555, 'r': 0.7137142857142857, 'f1': 0.6693461950696677}, 'argument': {'p': 0.6986016970465607, 'r': 0.672709466316863, 'f1': 0.6854111413132271}, 'combined': 0.4587773394963669, 'epoch': 28}
Chinese: {'event': {'p': 0.5443037974683544, 'r': 0.7962962962962963, 'f1': 0.6466165413533835}, 'argument': {'p': 0.6532575757575758, 'r': 0.5398822940145255, 'f1': 0.5911833264774442}, 'combined': 0.3822689178726331, 'epoch': 28}
Korean: {'event': {'p': 0.6415094339622641, 'r': 0.5396825396825397, 'f1': 0.5862068965517241}, 'argument': {'p': 0.7339650145772595, 'r': 0.49950396825396826, 'f1': 0.5944510035419126}, 'combined': 0.3484712779383625, 'epoch': 28}
Russian: {'event': {'p': 0.5625, 'r': 0.5, 'f1': 0.5294117647058824}, 'argument': {'p': 0.5889716384411344, 'r': 0.5602413146147377, 'f1': 0.5742473474801061}, 'combined': 0.304013301607115, 'epoch': 28}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6619639597961121, 'r': 0.6663803242424909, 'f1': 0.6641648004456004}, 'combined': 0.4438428226939293, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.67250045002088, 'r': 0.6807604350774312, 'f1': 0.6766052340494917}, 'combined': 0.45673953741282786, 'epoch': 16}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.664119066773934, 'r': 0.6202103681442523, 'f1': 0.6414141414141412}, 'combined': 0.43403964456596017, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5821052631578948, 'r': 0.7363515312916112, 'f1': 0.6502057613168725}, 'argument': {'p': 0.6758296223890353, 'r': 0.6699528430639133, 'f1': 0.672878401330568}, 'combined': 0.437509413210822, 'epoch': 25}
Test for Korean: {'event': {'p': 0.621268656716418, 'r': 0.7611428571428571, 'f1': 0.6841294298921418}, 'argument': {'p': 0.6879208268481474, 'r': 0.6918624021957774, 'f1': 0.6898859846462347}, 'combined': 0.4719713053666074, 'epoch': 25}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7129263470093921, 'r': 0.5194177671068427, 'f1': 0.6009792346690742}, 'combined': 0.36265988298995866, 'epoch': 25}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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
2023-01-23 17:18:28.769893: step: 2/1532, loss: 4.235422966303304e-05
2023-01-23 17:18:29.600138: step: 4/1532, loss: 0.08077888935804367
2023-01-23 17:18:30.401332: step: 6/1532, loss: 0.000318677572067827
2023-01-23 17:18:31.228500: step: 8/1532, loss: 9.074799891095608e-05
2023-01-23 17:18:32.030707: step: 10/1532, loss: 0.004432246088981628
2023-01-23 17:18:32.834830: step: 12/1532, loss: 0.10613885521888733
2023-01-23 17:18:33.641284: step: 14/1532, loss: 0.053472064435482025
2023-01-23 17:18:34.533382: step: 16/1532, loss: 0.003265658626332879
2023-01-23 17:18:35.353528: step: 18/1532, loss: 0.01498662680387497
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2023-01-23 17:39:24.452336: step: 3066/1532, loss: 0.00036635101423598826
==================================================
Loss: 0.065
--------------------
Dev Chinese: {'event': {'p': 0.6076923076923076, 'r': 0.7363515312916112, 'f1': 0.6658639373871162}, 'argument': {'p': 0.6605663223919018, 'r': 0.663986414297777, 'f1': 0.6622719528812945}, 'combined': 0.44098301016659347, 'epoch': 29}
Test Chinese: {'event': {'p': 0.6273562107298212, 'r': 0.7417142857142857, 'f1': 0.6797590992406388}, 'argument': {'p': 0.6727868778930106, 'r': 0.6923179177225549, 'f1': 0.6824126790411179}, 'combined': 0.4638762280153815, 'epoch': 29}
Dev Korean: {'event': {'p': 0.5863157894736842, 'r': 0.7416777629826897, 'f1': 0.6549088771310994}, 'argument': {'p': 0.6574721857088132, 'r': 0.6784654920739712, 'f1': 0.6678038914842374}, 'combined': 0.4373506967157204, 'epoch': 29}
Test Korean: {'event': {'p': 0.621268656716418, 'r': 0.7611428571428571, 'f1': 0.6841294298921418}, 'argument': {'p': 0.6708506463840919, 'r': 0.7086227495351853, 'f1': 0.6892195688215641}, 'combined': 0.4715153906884044, 'epoch': 29}
Dev Russian: {'event': {'p': 0.6238425925925926, 'r': 0.7177097203728362, 'f1': 0.6674922600619194}, 'argument': {'p': 0.662355542200245, 'r': 0.6628509464950844, 'f1': 0.6626031517487311}, 'combined': 0.44228247528491144, 'epoch': 29}
Test Russian: {'event': {'p': 0.6337033299697276, 'r': 0.7177142857142857, 'f1': 0.6730975348338694}, 'argument': {'p': 0.6812132328624839, 'r': 0.6883563286282561, 'f1': 0.6847661530616972}, 'combined': 0.46091440956350044, 'epoch': 29}
Chinese: {'event': {'p': 0.5569620253164557, 'r': 0.8148148148148148, 'f1': 0.6616541353383458}, 'argument': {'p': 0.664891395154553, 'r': 0.6264266036993309, 'f1': 0.6450861195542046}, 'combined': 0.426823898652406, 'epoch': 29}
Korean: {'event': {'p': 0.6415094339622641, 'r': 0.5396825396825397, 'f1': 0.5862068965517241}, 'argument': {'p': 0.6819794584500466, 'r': 0.5529563176622, 'f1': 0.6107278732388477}, 'combined': 0.35801289120897967, 'epoch': 29}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.5397198108405005, 'r': 0.5792115043166347, 'f1': 0.5587687453407535}, 'combined': 0.3122531223963035, 'epoch': 29}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6619639597961121, 'r': 0.6663803242424909, 'f1': 0.6641648004456004}, 'combined': 0.4438428226939293, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.67250045002088, 'r': 0.6807604350774312, 'f1': 0.6766052340494917}, 'combined': 0.45673953741282786, 'epoch': 16}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.664119066773934, 'r': 0.6202103681442523, 'f1': 0.6414141414141412}, 'combined': 0.43403964456596017, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5821052631578948, 'r': 0.7363515312916112, 'f1': 0.6502057613168725}, 'argument': {'p': 0.6758296223890353, 'r': 0.6699528430639133, 'f1': 0.672878401330568}, 'combined': 0.437509413210822, 'epoch': 25}
Test for Korean: {'event': {'p': 0.621268656716418, 'r': 0.7611428571428571, 'f1': 0.6841294298921418}, 'argument': {'p': 0.6879208268481474, 'r': 0.6918624021957774, 'f1': 0.6898859846462347}, 'combined': 0.4719713053666074, 'epoch': 25}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7129263470093921, 'r': 0.5194177671068427, 'f1': 0.6009792346690742}, 'combined': 0.36265988298995866, 'epoch': 25}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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
2023-01-23 17:44:34.341425: step: 2/1532, loss: 0.0008395418990403414
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2023-01-23 18:05:30.294083: step: 3066/1532, loss: 0.006727280560880899
==================================================
Loss: 0.089
--------------------
Dev Chinese: {'event': {'p': 0.6076923076923076, 'r': 0.7363515312916112, 'f1': 0.6658639373871162}, 'argument': {'p': 0.6784893791191579, 'r': 0.6454167801369832, 'f1': 0.6615399850912}, 'combined': 0.44049561921184066, 'epoch': 30}
Test Chinese: {'event': {'p': 0.6288061865635572, 'r': 0.7434285714285714, 'f1': 0.6813301911495155}, 'argument': {'p': 0.6903156795051696, 'r': 0.6785194443803375, 'f1': 0.6843667336287031}, 'combined': 0.4662797174396139, 'epoch': 30}
Dev Korean: {'event': {'p': 0.5936842105263158, 'r': 0.7509986684420772, 'f1': 0.6631393298059965}, 'argument': {'p': 0.6707320758280363, 'r': 0.6546811994666693, 'f1': 0.662609448854706}, 'combined': 0.43940238583663044, 'epoch': 30}
Test Korean: {'event': {'p': 0.6222014925373134, 'r': 0.7622857142857142, 'f1': 0.6851566512583461}, 'argument': {'p': 0.6916411135424988, 'r': 0.6944375061848539, 'f1': 0.6930364890237547}, 'combined': 0.4748385600193573, 'epoch': 30}
Dev Russian: {'event': {'p': 0.6261574074074074, 'r': 0.7203728362183754, 'f1': 0.669969040247678}, 'argument': {'p': 0.6683790222781428, 'r': 0.6389503936852992, 'f1': 0.6533334815248385}, 'combined': 0.4377132055788701, 'epoch': 30}
Test Russian: {'event': {'p': 0.6306760847628657, 'r': 0.7142857142857143, 'f1': 0.669882100750268}, 'argument': {'p': 0.6964089911932071, 'r': 0.6718327130274091, 'f1': 0.6839001332685203}, 'combined': 0.4581324579773046, 'epoch': 30}
Chinese: {'event': {'p': 0.5569620253164557, 'r': 0.8148148148148148, 'f1': 0.6616541353383458}, 'argument': {'p': 0.6627172017797017, 'r': 0.5257921600896808, 'f1': 0.5863672937405656}, 'combined': 0.38797234473059977, 'epoch': 30}
Korean: {'event': {'p': 0.6415094339622641, 'r': 0.5396825396825397, 'f1': 0.5862068965517241}, 'argument': {'p': 0.7520703933747412, 'r': 0.48048941798941797, 'f1': 0.5863599677158999}, 'combined': 0.34372825693690684, 'epoch': 30}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.509734049979059, 'r': 0.47243643656595713, 'f1': 0.4903770607393479}, 'combined': 0.27403423982492975, 'epoch': 30}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6619639597961121, 'r': 0.6663803242424909, 'f1': 0.6641648004456004}, 'combined': 0.4438428226939293, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.67250045002088, 'r': 0.6807604350774312, 'f1': 0.6766052340494917}, 'combined': 0.45673953741282786, 'epoch': 16}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.664119066773934, 'r': 0.6202103681442523, 'f1': 0.6414141414141412}, 'combined': 0.43403964456596017, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5821052631578948, 'r': 0.7363515312916112, 'f1': 0.6502057613168725}, 'argument': {'p': 0.6758296223890353, 'r': 0.6699528430639133, 'f1': 0.672878401330568}, 'combined': 0.437509413210822, 'epoch': 25}
Test for Korean: {'event': {'p': 0.621268656716418, 'r': 0.7611428571428571, 'f1': 0.6841294298921418}, 'argument': {'p': 0.6879208268481474, 'r': 0.6918624021957774, 'f1': 0.6898859846462347}, 'combined': 0.4719713053666074, 'epoch': 25}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7129263470093921, 'r': 0.5194177671068427, 'f1': 0.6009792346690742}, 'combined': 0.36265988298995866, 'epoch': 25}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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
2023-01-23 18:10:36.763464: step: 2/1532, loss: 0.4528963565826416
2023-01-23 18:10:37.567167: step: 4/1532, loss: 8.709122084837873e-06
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2023-01-23 18:10:39.187224: step: 8/1532, loss: 7.228180766105652e-05
2023-01-23 18:10:40.041362: step: 10/1532, loss: 9.284434781875461e-05
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2023-01-23 18:31:32.056342: step: 3066/1532, loss: 7.17830698704347e-05
==================================================
Loss: 0.059
--------------------
Dev Chinese: {'event': {'p': 0.6076923076923076, 'r': 0.7363515312916112, 'f1': 0.6658639373871162}, 'argument': {'p': 0.6957869054404949, 'r': 0.6411547484207227, 'f1': 0.6673545955034508}, 'combined': 0.444367358595314, 'epoch': 31}
Test Chinese: {'event': {'p': 0.6297728371193814, 'r': 0.7445714285714286, 'f1': 0.6823775857554334}, 'argument': {'p': 0.7033223397018428, 'r': 0.6582808063031288, 'f1': 0.6800565909800588}, 'combined': 0.46405537473004277, 'epoch': 31}
Dev Korean: {'event': {'p': 0.588421052631579, 'r': 0.7443408788282291, 'f1': 0.6572604350382129}, 'argument': {'p': 0.6844841467903129, 'r': 0.6536649622579968, 'f1': 0.668719653067802}, 'combined': 0.4395229700939463, 'epoch': 31}
Test Korean: {'event': {'p': 0.621268656716418, 'r': 0.7611428571428571, 'f1': 0.6841294298921418}, 'argument': {'p': 0.7022152459686068, 'r': 0.6721169212173368, 'f1': 0.6868365020061973}, 'combined': 0.4698850645466126, 'epoch': 31}
Dev Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.6893770133335347, 'r': 0.6384073909884862, 'f1': 0.6629139174358922}, 'combined': 0.44495274705913757, 'epoch': 31}
Test Russian: {'event': {'p': 0.6306760847628657, 'r': 0.7142857142857143, 'f1': 0.669882100750268}, 'argument': {'p': 0.707452490788538, 'r': 0.6555066785278412, 'f1': 0.6804896916840747}, 'combined': 0.4558478642042301, 'epoch': 31}
Chinese: {'event': {'p': 0.5569620253164557, 'r': 0.8148148148148148, 'f1': 0.6616541353383458}, 'argument': {'p': 0.6635745207173779, 'r': 0.5374405209115953, 'f1': 0.5938840459388406}, 'combined': 0.39294583490690205, 'epoch': 31}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7399068322981366, 'r': 0.48622448979591837, 'f1': 0.5868226600985221}, 'combined': 0.35411712247324617, 'epoch': 31}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.5244712850524864, 'r': 0.4605113722412076, 'f1': 0.4904147081010263}, 'combined': 0.2740552780564559, 'epoch': 31}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6619639597961121, 'r': 0.6663803242424909, 'f1': 0.6641648004456004}, 'combined': 0.4438428226939293, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.67250045002088, 'r': 0.6807604350774312, 'f1': 0.6766052340494917}, 'combined': 0.45673953741282786, 'epoch': 16}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.664119066773934, 'r': 0.6202103681442523, 'f1': 0.6414141414141412}, 'combined': 0.43403964456596017, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5821052631578948, 'r': 0.7363515312916112, 'f1': 0.6502057613168725}, 'argument': {'p': 0.6758296223890353, 'r': 0.6699528430639133, 'f1': 0.672878401330568}, 'combined': 0.437509413210822, 'epoch': 25}
Test for Korean: {'event': {'p': 0.621268656716418, 'r': 0.7611428571428571, 'f1': 0.6841294298921418}, 'argument': {'p': 0.6879208268481474, 'r': 0.6918624021957774, 'f1': 0.6898859846462347}, 'combined': 0.4719713053666074, 'epoch': 25}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7129263470093921, 'r': 0.5194177671068427, 'f1': 0.6009792346690742}, 'combined': 0.36265988298995866, 'epoch': 25}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, 'epoch': 7}
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Epoch: 32
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 18:57:24.526286: step: 3050/1532, loss: 0.34706929326057434
2023-01-23 18:57:25.329512: step: 3052/1532, loss: 0.0012699274811893702
2023-01-23 18:57:26.116581: step: 3054/1532, loss: 0.001029502134770155
2023-01-23 18:57:26.957926: step: 3056/1532, loss: 0.26530465483665466
2023-01-23 18:57:27.775079: step: 3058/1532, loss: 0.21654149889945984
2023-01-23 18:57:28.606802: step: 3060/1532, loss: 0.16012735664844513
2023-01-23 18:57:29.421678: step: 3062/1532, loss: 0.08181968331336975
2023-01-23 18:57:30.252060: step: 3064/1532, loss: 0.0006290011806413531
2023-01-23 18:57:30.846239: step: 3066/1532, loss: 1.325606535829138e-05
==================================================
Loss: 0.065
--------------------
Dev Chinese: {'event': {'p': 0.6043956043956044, 'r': 0.7323568575233023, 'f1': 0.6622516556291391}, 'argument': {'p': 0.6887533644104433, 'r': 0.637885316817353, 'f1': 0.6623441096401043}, 'combined': 0.4386384832053671, 'epoch': 32}
Test Chinese: {'event': {'p': 0.6302561623972934, 'r': 0.7451428571428571, 'f1': 0.6829012830583923}, 'argument': {'p': 0.6902997425677523, 'r': 0.6700106865661027, 'f1': 0.6800039087382646}, 'combined': 0.46437554176208273, 'epoch': 32}
Dev Korean: {'event': {'p': 0.5873684210526315, 'r': 0.7430093209054593, 'f1': 0.656084656084656}, 'argument': {'p': 0.6857642121982332, 'r': 0.6623915378158424, 'f1': 0.6738752715957557}, 'combined': 0.44211922580885554, 'epoch': 32}
Test Korean: {'event': {'p': 0.6198694029850746, 'r': 0.7594285714285715, 'f1': 0.6825885978428352}, 'argument': {'p': 0.6952028139540491, 'r': 0.6858240238838427, 'f1': 0.6904815724331388}, 'combined': 0.4713148483634523, 'epoch': 32}
Dev Russian: {'event': {'p': 0.6203703703703703, 'r': 0.7137150466045273, 'f1': 0.6637770897832818}, 'argument': {'p': 0.6883397691993786, 'r': 0.6472141668573694, 'f1': 0.6671437793856948}, 'combined': 0.44283475634765623, 'epoch': 32}
Test Russian: {'event': {'p': 0.6316851664984864, 'r': 0.7154285714285714, 'f1': 0.6709539121114683}, 'argument': {'p': 0.6954177234550526, 'r': 0.6667356922185282, 'f1': 0.6807747378434009}, 'combined': 0.4567684736226891, 'epoch': 32}
Chinese: {'event': {'p': 0.5443037974683544, 'r': 0.7962962962962963, 'f1': 0.6466165413533835}, 'argument': {'p': 0.6487937628714328, 'r': 0.5522789882294015, 'f1': 0.5966585497835497}, 'combined': 0.38580928782996454, 'epoch': 32}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.703416149068323, 'r': 0.4622448979591836, 'f1': 0.5578817733990147}, 'combined': 0.33665279429250894, 'epoch': 32}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.5396147309926509, 'r': 0.5264533960903912, 'f1': 0.5329528207334824}, 'combined': 0.29782657629224024, 'epoch': 32}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6619639597961121, 'r': 0.6663803242424909, 'f1': 0.6641648004456004}, 'combined': 0.4438428226939293, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.67250045002088, 'r': 0.6807604350774312, 'f1': 0.6766052340494917}, 'combined': 0.45673953741282786, 'epoch': 16}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.664119066773934, 'r': 0.6202103681442523, 'f1': 0.6414141414141412}, 'combined': 0.43403964456596017, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5821052631578948, 'r': 0.7363515312916112, 'f1': 0.6502057613168725}, 'argument': {'p': 0.6758296223890353, 'r': 0.6699528430639133, 'f1': 0.672878401330568}, 'combined': 0.437509413210822, 'epoch': 25}
Test for Korean: {'event': {'p': 0.621268656716418, 'r': 0.7611428571428571, 'f1': 0.6841294298921418}, 'argument': {'p': 0.6879208268481474, 'r': 0.6918624021957774, 'f1': 0.6898859846462347}, 'combined': 0.4719713053666074, 'epoch': 25}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7129263470093921, 'r': 0.5194177671068427, 'f1': 0.6009792346690742}, 'combined': 0.36265988298995866, 'epoch': 25}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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
2023-01-23 19:02:34.728175: step: 2/1532, loss: 0.2576027512550354
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2023-01-23 19:23:26.098448: step: 3066/1532, loss: -2.4509513423254248e-06
==================================================
Loss: 0.059
--------------------
Dev Chinese: {'event': {'p': 0.6043956043956044, 'r': 0.7323568575233023, 'f1': 0.6622516556291391}, 'argument': {'p': 0.6466558357576668, 'r': 0.6609941026259079, 'f1': 0.6537463602725536}, 'combined': 0.4329446094520223, 'epoch': 33}
Test Chinese: {'event': {'p': 0.6259062348960851, 'r': 0.74, 'f1': 0.6781880073317623}, 'argument': {'p': 0.6766745616692214, 'r': 0.679446871242465, 'f1': 0.6780578827492947}, 'combined': 0.4598507243573379, 'epoch': 33}
Dev Korean: {'event': {'p': 0.5894736842105263, 'r': 0.7456724367509987, 'f1': 0.6584362139917695}, 'argument': {'p': 0.642854545813246, 'r': 0.6778176284354313, 'f1': 0.6598732859953828}, 'combined': 0.434484468145108, 'epoch': 33}
Test Korean: {'event': {'p': 0.621268656716418, 'r': 0.7611428571428571, 'f1': 0.6841294298921418}, 'argument': {'p': 0.6752397999795606, 'r': 0.6950662807754505, 'f1': 0.6850096091593747}, 'combined': 0.46863523338484186, 'epoch': 33}
Dev Russian: {'event': {'p': 0.6226851851851852, 'r': 0.7163781624500666, 'f1': 0.6662538699690402}, 'argument': {'p': 0.6450094768664714, 'r': 0.6604357124865963, 'f1': 0.6526314500052185}, 'combined': 0.43481822922948304, 'epoch': 33}
Test Russian: {'event': {'p': 0.6316851664984864, 'r': 0.7154285714285714, 'f1': 0.6709539121114683}, 'argument': {'p': 0.6804013572525114, 'r': 0.6737470652500418, 'f1': 0.6770578616394769}, 'combined': 0.4542746209928322, 'epoch': 33}
Chinese: {'event': {'p': 0.5569620253164557, 'r': 0.8148148148148148, 'f1': 0.6616541353383458}, 'argument': {'p': 0.6637628048519137, 'r': 0.5540499445458122, 'f1': 0.6039643539643539}, 'combined': 0.39961551239746723, 'epoch': 33}
Korean: {'event': {'p': 0.6226415094339622, 'r': 0.5238095238095238, 'f1': 0.5689655172413793}, 'argument': {'p': 0.6524366156719098, 'r': 0.47784090161886356, 'f1': 0.5516537238201514}, 'combined': 0.3138719463114655, 'epoch': 33}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.5290156203949308, 'r': 0.5548212604141957, 'f1': 0.541611230404334}, 'combined': 0.3026650993435985, 'epoch': 33}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6619639597961121, 'r': 0.6663803242424909, 'f1': 0.6641648004456004}, 'combined': 0.4438428226939293, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.67250045002088, 'r': 0.6807604350774312, 'f1': 0.6766052340494917}, 'combined': 0.45673953741282786, 'epoch': 16}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.664119066773934, 'r': 0.6202103681442523, 'f1': 0.6414141414141412}, 'combined': 0.43403964456596017, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5821052631578948, 'r': 0.7363515312916112, 'f1': 0.6502057613168725}, 'argument': {'p': 0.6758296223890353, 'r': 0.6699528430639133, 'f1': 0.672878401330568}, 'combined': 0.437509413210822, 'epoch': 25}
Test for Korean: {'event': {'p': 0.621268656716418, 'r': 0.7611428571428571, 'f1': 0.6841294298921418}, 'argument': {'p': 0.6879208268481474, 'r': 0.6918624021957774, 'f1': 0.6898859846462347}, 'combined': 0.4719713053666074, 'epoch': 25}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7129263470093921, 'r': 0.5194177671068427, 'f1': 0.6009792346690742}, 'combined': 0.36265988298995866, 'epoch': 25}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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
2023-01-23 19:28:32.377593: step: 2/1532, loss: 0.00041145674185827374
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2023-01-23 19:49:20.664064: step: 3058/1532, loss: 0.025362344458699226
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2023-01-23 19:49:23.070008: step: 3064/1532, loss: 0.0006236759945750237
2023-01-23 19:49:23.668733: step: 3066/1532, loss: 0.2110235095024109
==================================================
Loss: 0.052
--------------------
Dev Chinese: {'event': {'p': 0.6065934065934065, 'r': 0.7350199733688415, 'f1': 0.6646598434677904}, 'argument': {'p': 0.6715595561118254, 'r': 0.6402433073812221, 'f1': 0.6555276303691028}, 'combined': 0.4357028921899394, 'epoch': 34}
Test Chinese: {'event': {'p': 0.625422909618173, 'r': 0.7394285714285714, 'f1': 0.6776643100288033}, 'argument': {'p': 0.7022058474875669, 'r': 0.6685499698264031, 'f1': 0.6849647358340665}, 'combined': 0.4641761551030542, 'epoch': 34}
Dev Korean: {'event': {'p': 0.5873684210526315, 'r': 0.7430093209054593, 'f1': 0.656084656084656}, 'argument': {'p': 0.6712020544581151, 'r': 0.648325697272088, 'f1': 0.6595655747238726}, 'combined': 0.43273085325799043, 'epoch': 34}
Test Korean: {'event': {'p': 0.6203358208955224, 'r': 0.76, 'f1': 0.6831022085259374}, 'argument': {'p': 0.6994565518809608, 'r': 0.6805587051567201, 'f1': 0.6898782355251791}, 'combined': 0.4712573463012267, 'epoch': 34}
Dev Russian: {'event': {'p': 0.6261574074074074, 'r': 0.7203728362183754, 'f1': 0.669969040247678}, 'argument': {'p': 0.6720668414960032, 'r': 0.6422981150333424, 'f1': 0.6568453660072262}, 'combined': 0.4400660594549961, 'epoch': 34}
Test Russian: {'event': {'p': 0.6316851664984864, 'r': 0.7154285714285714, 'f1': 0.6709539121114683}, 'argument': {'p': 0.7046260183090545, 'r': 0.665178795450605, 'f1': 0.6843344123102336}, 'combined': 0.4591568511320538, 'epoch': 34}
Chinese: {'event': {'p': 0.5316455696202531, 'r': 0.7777777777777778, 'f1': 0.6315789473684209}, 'argument': {'p': 0.7174168873198, 'r': 0.6106937139994992, 'f1': 0.6597673160173161}, 'combined': 0.4166951469583048, 'epoch': 34}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.687202788091981, 'r': 0.42213885554221686, 'f1': 0.5230038918222155}, 'combined': 0.31560579678926803, 'epoch': 34}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.5694416523182403, 'r': 0.527775189953491, 'f1': 0.5478172857745096}, 'combined': 0.30613318910928483, 'epoch': 34}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6619639597961121, 'r': 0.6663803242424909, 'f1': 0.6641648004456004}, 'combined': 0.4438428226939293, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.67250045002088, 'r': 0.6807604350774312, 'f1': 0.6766052340494917}, 'combined': 0.45673953741282786, 'epoch': 16}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.664119066773934, 'r': 0.6202103681442523, 'f1': 0.6414141414141412}, 'combined': 0.43403964456596017, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5821052631578948, 'r': 0.7363515312916112, 'f1': 0.6502057613168725}, 'argument': {'p': 0.6758296223890353, 'r': 0.6699528430639133, 'f1': 0.672878401330568}, 'combined': 0.437509413210822, 'epoch': 25}
Test for Korean: {'event': {'p': 0.621268656716418, 'r': 0.7611428571428571, 'f1': 0.6841294298921418}, 'argument': {'p': 0.6879208268481474, 'r': 0.6918624021957774, 'f1': 0.6898859846462347}, 'combined': 0.4719713053666074, 'epoch': 25}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7129263470093921, 'r': 0.5194177671068427, 'f1': 0.6009792346690742}, 'combined': 0.36265988298995866, 'epoch': 25}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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
2023-01-23 19:54:27.427940: step: 2/1532, loss: 0.030986711382865906
2023-01-23 19:54:28.274147: step: 4/1532, loss: 0.0008995032985694706
2023-01-23 19:54:29.075250: step: 6/1532, loss: 0.02411429025232792
2023-01-23 19:54:29.873516: step: 8/1532, loss: 0.0018555554561316967
2023-01-23 19:54:30.705126: step: 10/1532, loss: 0.010187072679400444
2023-01-23 19:54:31.541819: step: 12/1532, loss: 0.0388433039188385
2023-01-23 19:54:32.335971: step: 14/1532, loss: 0.0018595298752188683
2023-01-23 19:54:33.156766: step: 16/1532, loss: 0.004540718160569668
2023-01-23 19:54:33.977598: step: 18/1532, loss: 0.0011543843429535627
2023-01-23 19:54:34.778471: step: 20/1532, loss: 0.001629140111617744
2023-01-23 19:54:35.622338: step: 22/1532, loss: 0.0008880671230144799
2023-01-23 19:54:36.429678: step: 24/1532, loss: 0.00961183849722147
2023-01-23 19:54:37.227353: step: 26/1532, loss: 0.04920611530542374
2023-01-23 19:54:38.049327: step: 28/1532, loss: 0.003910667262971401
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2023-01-23 19:54:40.479933: step: 34/1532, loss: 0.04935525730252266
2023-01-23 19:54:41.295893: step: 36/1532, loss: 8.800987416179851e-05
2023-01-23 19:54:42.112251: step: 38/1532, loss: 0.0002609228540677577
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2023-01-23 19:54:45.373579: step: 46/1532, loss: 0.0013630277244374156
2023-01-23 19:54:46.168915: step: 48/1532, loss: 0.004419277422130108
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==================================================
Loss: 0.049
--------------------
Dev Chinese: {'event': {'p': 0.6032967032967033, 'r': 0.7310252996005326, 'f1': 0.6610475617098133}, 'argument': {'p': 0.6686270283768238, 'r': 0.6592306426230075, 'f1': 0.6638955894423137}, 'combined': 0.43886656063074075, 'epoch': 35}
Test Chinese: {'event': {'p': 0.6278395360077332, 'r': 0.7422857142857143, 'f1': 0.6802827965435979}, 'argument': {'p': 0.6807209795556036, 'r': 0.6849071693854639, 'f1': 0.6828076583076474}, 'combined': 0.4645023032949119, 'epoch': 35}
Dev Korean: {'event': {'p': 0.5852631578947368, 'r': 0.7403462050599201, 'f1': 0.6537330981775425}, 'argument': {'p': 0.6685572543990222, 'r': 0.6714682438956798, 'f1': 0.6700095873195124}, 'combined': 0.4380074433270416, 'epoch': 35}
Test Korean: {'event': {'p': 0.6208022388059702, 'r': 0.7605714285714286, 'f1': 0.6836158192090396}, 'argument': {'p': 0.6807545554501067, 'r': 0.7014182687689968, 'f1': 0.6909319491362378}, 'combined': 0.47233201042646766, 'epoch': 35}
Dev Russian: {'event': {'p': 0.6215277777777778, 'r': 0.715046604527297, 'f1': 0.6650154798761609}, 'argument': {'p': 0.672383669520546, 'r': 0.6623180457253282, 'f1': 0.6673129027820803}, 'combined': 0.44377341027117906, 'epoch': 35}
Test Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6866984243906006, 'r': 0.6811818111314375, 'f1': 0.6839289936066301}, 'combined': 0.4592513552996288, 'epoch': 35}
Chinese: {'event': {'p': 0.5569620253164557, 'r': 0.8148148148148148, 'f1': 0.6616541353383458}, 'argument': {'p': 0.675361418416974, 'r': 0.6028019271820925, 'f1': 0.6370221239216872}, 'combined': 0.42148832259480057, 'epoch': 35}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.68207779565727, 'r': 0.4579665199413099, 'f1': 0.547994126425499}, 'combined': 0.3306861107740081, 'epoch': 35}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.5810123610348793, 'r': 0.5810123610348793, 'f1': 0.5810123610348793}, 'combined': 0.3246833782253738, 'epoch': 35}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6619639597961121, 'r': 0.6663803242424909, 'f1': 0.6641648004456004}, 'combined': 0.4438428226939293, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.67250045002088, 'r': 0.6807604350774312, 'f1': 0.6766052340494917}, 'combined': 0.45673953741282786, 'epoch': 16}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.664119066773934, 'r': 0.6202103681442523, 'f1': 0.6414141414141412}, 'combined': 0.43403964456596017, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5821052631578948, 'r': 0.7363515312916112, 'f1': 0.6502057613168725}, 'argument': {'p': 0.6758296223890353, 'r': 0.6699528430639133, 'f1': 0.672878401330568}, 'combined': 0.437509413210822, 'epoch': 25}
Test for Korean: {'event': {'p': 0.621268656716418, 'r': 0.7611428571428571, 'f1': 0.6841294298921418}, 'argument': {'p': 0.6879208268481474, 'r': 0.6918624021957774, 'f1': 0.6898859846462347}, 'combined': 0.4719713053666074, 'epoch': 25}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7129263470093921, 'r': 0.5194177671068427, 'f1': 0.6009792346690742}, 'combined': 0.36265988298995866, 'epoch': 25}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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
2023-01-23 20:20:23.923485: step: 2/1532, loss: 0.8423208594322205
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2023-01-23 20:41:15.514413: step: 3066/1532, loss: 0.00011345342500135303
==================================================
Loss: 0.049
--------------------
Dev Chinese: {'event': {'p': 0.6087912087912087, 'r': 0.7376830892143809, 'f1': 0.6670680313064418}, 'argument': {'p': 0.6831407198179863, 'r': 0.644234036810601, 'f1': 0.6631171813992635}, 'combined': 0.4423442727214833, 'epoch': 36}
Test Chinese: {'event': {'p': 0.624939584340261, 'r': 0.7388571428571429, 'f1': 0.6771406127258445}, 'argument': {'p': 0.6929889653718997, 'r': 0.6667537078151873, 'f1': 0.679618241374632}, 'combined': 0.4601971123840792, 'epoch': 36}
Dev Korean: {'event': {'p': 0.5905263157894737, 'r': 0.7470039946737683, 'f1': 0.6596119929453264}, 'argument': {'p': 0.6765567127981544, 'r': 0.6554603105861744, 'f1': 0.6658414499435245}, 'combined': 0.4391970057828539, 'epoch': 36}
Test Korean: {'event': {'p': 0.6184701492537313, 'r': 0.7577142857142857, 'f1': 0.6810477657935284}, 'argument': {'p': 0.6912342625340286, 'r': 0.6814526456113773, 'f1': 0.6863086027059953}, 'combined': 0.4674089405177964, 'epoch': 36}
Dev Russian: {'event': {'p': 0.625, 'r': 0.7190412782956058, 'f1': 0.6687306501547987}, 'argument': {'p': 0.680950502653787, 'r': 0.6458079561443544, 'f1': 0.6629138098771606}, 'combined': 0.4433107830757483, 'epoch': 36}
Test Russian: {'event': {'p': 0.6316851664984864, 'r': 0.7154285714285714, 'f1': 0.6709539121114683}, 'argument': {'p': 0.7027125444084233, 'r': 0.6637138377553904, 'f1': 0.6826566667274075}, 'combined': 0.45803116116972886, 'epoch': 36}
Chinese: {'event': {'p': 0.5569620253164557, 'r': 0.8148148148148148, 'f1': 0.6616541353383458}, 'argument': {'p': 0.6715421542154215, 'r': 0.5605434510393188, 'f1': 0.6110428610428611}, 'combined': 0.40429903587798327, 'epoch': 36}
Korean: {'event': {'p': 0.6415094339622641, 'r': 0.5396825396825397, 'f1': 0.5862068965517241}, 'argument': {'p': 0.6704660587639311, 'r': 0.4376653439153439, 'f1': 0.5296118447378951}, 'combined': 0.31046211588083505, 'epoch': 36}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.5387833763770795, 'r': 0.6044886661791624, 'f1': 0.5697479382378313}, 'combined': 0.3183885537211411, 'epoch': 36}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6619639597961121, 'r': 0.6663803242424909, 'f1': 0.6641648004456004}, 'combined': 0.4438428226939293, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.67250045002088, 'r': 0.6807604350774312, 'f1': 0.6766052340494917}, 'combined': 0.45673953741282786, 'epoch': 16}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.664119066773934, 'r': 0.6202103681442523, 'f1': 0.6414141414141412}, 'combined': 0.43403964456596017, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5821052631578948, 'r': 0.7363515312916112, 'f1': 0.6502057613168725}, 'argument': {'p': 0.6758296223890353, 'r': 0.6699528430639133, 'f1': 0.672878401330568}, 'combined': 0.437509413210822, 'epoch': 25}
Test for Korean: {'event': {'p': 0.621268656716418, 'r': 0.7611428571428571, 'f1': 0.6841294298921418}, 'argument': {'p': 0.6879208268481474, 'r': 0.6918624021957774, 'f1': 0.6898859846462347}, 'combined': 0.4719713053666074, 'epoch': 25}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7129263470093921, 'r': 0.5194177671068427, 'f1': 0.6009792346690742}, 'combined': 0.36265988298995866, 'epoch': 25}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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
2023-01-23 20:46:19.512389: step: 2/1532, loss: 0.0034508495591580868
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2023-01-23 20:46:21.956019: step: 8/1532, loss: 7.334932161029428e-05
2023-01-23 20:46:22.753074: step: 10/1532, loss: 9.43621762417024e-06
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2023-01-23 20:46:25.195293: step: 16/1532, loss: 0.04769422858953476
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2023-01-23 21:07:09.832138: step: 3066/1532, loss: 9.983738209484727e-07
==================================================
Loss: 0.045
--------------------
Dev Chinese: {'event': {'p': 0.6087912087912087, 'r': 0.7376830892143809, 'f1': 0.6670680313064418}, 'argument': {'p': 0.6851862816014147, 'r': 0.6420449231302146, 'f1': 0.6629144521803362}, 'combined': 0.4422090385405252, 'epoch': 37}
Test Chinese: {'event': {'p': 0.626389560173997, 'r': 0.7405714285714285, 'f1': 0.6787117046347211}, 'argument': {'p': 0.698211786601172, 'r': 0.6690897907155846, 'f1': 0.6833406556714683}, 'combined': 0.46379130125699025, 'epoch': 37}
Dev Korean: {'event': {'p': 0.5852631578947368, 'r': 0.7403462050599201, 'f1': 0.6537330981775425}, 'argument': {'p': 0.6921633632728607, 'r': 0.6510049906924584, 'f1': 0.6709535741142751}, 'combined': 0.4386245587390205, 'epoch': 37}
Test Korean: {'event': {'p': 0.6184701492537313, 'r': 0.7577142857142857, 'f1': 0.6810477657935284}, 'argument': {'p': 0.6967764850028886, 'r': 0.6824317784267239, 'f1': 0.6895295343489796}, 'combined': 0.46960254881702457, 'epoch': 37}
Dev Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.6847431069028976, 'r': 0.6400524465123859, 'f1': 0.6616439792175343}, 'combined': 0.4441003550909023, 'epoch': 37}
Test Russian: {'event': {'p': 0.6316851664984864, 'r': 0.7154285714285714, 'f1': 0.6709539121114683}, 'argument': {'p': 0.7048780767475977, 'r': 0.6642120338583133, 'f1': 0.6839411041709363}, 'combined': 0.458892959497327, 'epoch': 37}
Chinese: {'event': {'p': 0.5569620253164557, 'r': 0.8148148148148148, 'f1': 0.6616541353383458}, 'argument': {'p': 0.687068706870687, 'r': 0.573503631354871, 'f1': 0.6251706251706252}, 'combined': 0.41364672943620306, 'epoch': 37}
Korean: {'event': {'p': 0.6415094339622641, 'r': 0.5396825396825397, 'f1': 0.5862068965517241}, 'argument': {'p': 0.7049382716049383, 'r': 0.44058641975308643, 'f1': 0.5422602089268757}, 'combined': 0.3178766741985133, 'epoch': 37}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.535479797979798, 'r': 0.5746612466124661, 'f1': 0.5543790849673202}, 'combined': 0.3098000768935026, 'epoch': 37}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6619639597961121, 'r': 0.6663803242424909, 'f1': 0.6641648004456004}, 'combined': 0.4438428226939293, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.67250045002088, 'r': 0.6807604350774312, 'f1': 0.6766052340494917}, 'combined': 0.45673953741282786, 'epoch': 16}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.664119066773934, 'r': 0.6202103681442523, 'f1': 0.6414141414141412}, 'combined': 0.43403964456596017, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5821052631578948, 'r': 0.7363515312916112, 'f1': 0.6502057613168725}, 'argument': {'p': 0.6758296223890353, 'r': 0.6699528430639133, 'f1': 0.672878401330568}, 'combined': 0.437509413210822, 'epoch': 25}
Test for Korean: {'event': {'p': 0.621268656716418, 'r': 0.7611428571428571, 'f1': 0.6841294298921418}, 'argument': {'p': 0.6879208268481474, 'r': 0.6918624021957774, 'f1': 0.6898859846462347}, 'combined': 0.4719713053666074, 'epoch': 25}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7129263470093921, 'r': 0.5194177671068427, 'f1': 0.6009792346690742}, 'combined': 0.36265988298995866, 'epoch': 25}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, '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
2023-01-23 21:12:13.119033: step: 2/1532, loss: 7.949050996103324e-06
2023-01-23 21:12:13.924996: step: 4/1532, loss: 0.00594710698351264
2023-01-23 21:12:14.719634: step: 6/1532, loss: 0.0012771834153681993
2023-01-23 21:12:15.508185: step: 8/1532, loss: 5.114227315061726e-05
2023-01-23 21:12:16.310899: step: 10/1532, loss: 3.5082546673947945e-05
2023-01-23 21:12:17.123004: step: 12/1532, loss: 0.004915275145322084
2023-01-23 21:12:17.969060: step: 14/1532, loss: 0.1363958865404129
2023-01-23 21:12:18.862705: step: 16/1532, loss: 0.023038432002067566
2023-01-23 21:12:19.669412: step: 18/1532, loss: 0.0013907973188906908
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2023-01-23 21:33:03.978665: step: 3066/1532, loss: -7.312695379368961e-05
==================================================
Loss: 0.041
--------------------
Dev Chinese: {'event': {'p': 0.6065934065934065, 'r': 0.7350199733688415, 'f1': 0.6646598434677904}, 'argument': {'p': 0.6700694224273155, 'r': 0.6586703124304011, 'f1': 0.6643209715821725}, 'combined': 0.44154747298417724, 'epoch': 38}
Test Chinese: {'event': {'p': 0.623972933784437, 'r': 0.7377142857142858, 'f1': 0.6760932181199268}, 'argument': {'p': 0.6769780701263884, 'r': 0.6848257531759272, 'f1': 0.6808792996830488}, 'combined': 0.46033787687395455, 'epoch': 38}
Dev Korean: {'event': {'p': 0.5821052631578948, 'r': 0.7363515312916112, 'f1': 0.6502057613168725}, 'argument': {'p': 0.6691946538875495, 'r': 0.665800188251888, 'f1': 0.6674931055485692}, 'combined': 0.43400786286697096, 'epoch': 38}
Test Korean: {'event': {'p': 0.6208022388059702, 'r': 0.7605714285714286, 'f1': 0.6836158192090396}, 'argument': {'p': 0.67706738085415, 'r': 0.7015259256723113, 'f1': 0.6890796855716751}, 'combined': 0.47106577375238806, 'epoch': 38}
Dev Russian: {'event': {'p': 0.625, 'r': 0.7190412782956058, 'f1': 0.6687306501547987}, 'argument': {'p': 0.6711290414888166, 'r': 0.6550420532284105, 'f1': 0.662987976542781}, 'combined': 0.4433603805982684, 'epoch': 38}
Test Russian: {'event': {'p': 0.6316851664984864, 'r': 0.7154285714285714, 'f1': 0.6709539121114683}, 'argument': {'p': 0.6800239568886313, 'r': 0.681926121803005, 'f1': 0.6809737110183639}, 'combined': 0.45690197545283573, 'epoch': 38}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.6626721763085399, 'r': 0.6024292511895818, 'f1': 0.6311163583890856}, 'combined': 0.4270712199625391, 'epoch': 38}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.6979458450046685, 'r': 0.49437830687830686, 'f1': 0.5787843592721642}, 'combined': 0.34926642369871985, 'epoch': 38}
Russian: {'event': {'p': 0.5625, 'r': 0.5, 'f1': 0.5294117647058824}, 'argument': {'p': 0.5341835790512615, 'r': 0.5742473474801061, 'f1': 0.5534914192579337}, 'combined': 0.29302486901890606, 'epoch': 38}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6619639597961121, 'r': 0.6663803242424909, 'f1': 0.6641648004456004}, 'combined': 0.4438428226939293, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.67250045002088, 'r': 0.6807604350774312, 'f1': 0.6766052340494917}, 'combined': 0.45673953741282786, 'epoch': 16}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.664119066773934, 'r': 0.6202103681442523, 'f1': 0.6414141414141412}, 'combined': 0.43403964456596017, 'epoch': 16}
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Dev for Korean: {'event': {'p': 0.5821052631578948, 'r': 0.7363515312916112, 'f1': 0.6502057613168725}, 'argument': {'p': 0.6758296223890353, 'r': 0.6699528430639133, 'f1': 0.672878401330568}, 'combined': 0.437509413210822, 'epoch': 25}
Test for Korean: {'event': {'p': 0.621268656716418, 'r': 0.7611428571428571, 'f1': 0.6841294298921418}, 'argument': {'p': 0.6879208268481474, 'r': 0.6918624021957774, 'f1': 0.6898859846462347}, 'combined': 0.4719713053666074, 'epoch': 25}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7129263470093921, 'r': 0.5194177671068427, 'f1': 0.6009792346690742}, 'combined': 0.36265988298995866, 'epoch': 25}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, 'epoch': 7}
******************************
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
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2023-01-23 21:58:26.857726: step: 2982/1532, loss: 0.0001093849350581877
2023-01-23 21:58:27.673191: step: 2984/1532, loss: 0.12027530372142792
2023-01-23 21:58:28.480592: step: 2986/1532, loss: 0.0019606815185397863
2023-01-23 21:58:29.288050: step: 2988/1532, loss: 5.2775252697756514e-05
2023-01-23 21:58:30.121069: step: 2990/1532, loss: 0.0002906367590185255
2023-01-23 21:58:30.954331: step: 2992/1532, loss: 0.4558141231536865
2023-01-23 21:58:31.747654: step: 2994/1532, loss: 4.3222433305345476e-05
2023-01-23 21:58:32.576448: step: 2996/1532, loss: 0.004731818102300167
2023-01-23 21:58:33.419690: step: 2998/1532, loss: 0.03223133459687233
2023-01-23 21:58:34.210595: step: 3000/1532, loss: 0.0945114865899086
2023-01-23 21:58:34.998161: step: 3002/1532, loss: 0.16510659456253052
2023-01-23 21:58:35.777199: step: 3004/1532, loss: 0.009169168770313263
2023-01-23 21:58:36.605843: step: 3006/1532, loss: 0.42190977931022644
2023-01-23 21:58:37.458056: step: 3008/1532, loss: 5.8291472669225186e-05
2023-01-23 21:58:38.276793: step: 3010/1532, loss: 6.372977804858238e-05
2023-01-23 21:58:39.090252: step: 3012/1532, loss: 0.0006524129421450198
2023-01-23 21:58:39.896163: step: 3014/1532, loss: 0.0005307892570272088
2023-01-23 21:58:40.719844: step: 3016/1532, loss: 0.04888058081269264
2023-01-23 21:58:41.538437: step: 3018/1532, loss: 0.006289622746407986
2023-01-23 21:58:42.340338: step: 3020/1532, loss: 0.017014559358358383
2023-01-23 21:58:43.138875: step: 3022/1532, loss: 0.09037382155656815
2023-01-23 21:58:43.956015: step: 3024/1532, loss: 0.00046497434959746897
2023-01-23 21:58:44.792837: step: 3026/1532, loss: 0.06303084641695023
2023-01-23 21:58:45.623062: step: 3028/1532, loss: 0.000589295756071806
2023-01-23 21:58:46.433379: step: 3030/1532, loss: 0.19137732684612274
2023-01-23 21:58:47.270296: step: 3032/1532, loss: 0.005776435602456331
2023-01-23 21:58:48.096888: step: 3034/1532, loss: 0.10550101101398468
2023-01-23 21:58:48.939784: step: 3036/1532, loss: 0.00012120499013690278
2023-01-23 21:58:49.761982: step: 3038/1532, loss: 0.4000418484210968
2023-01-23 21:58:50.569098: step: 3040/1532, loss: 0.0006229235441423953
2023-01-23 21:58:51.408494: step: 3042/1532, loss: 0.0003395870444364846
2023-01-23 21:58:52.214348: step: 3044/1532, loss: 0.000474423635751009
2023-01-23 21:58:53.050209: step: 3046/1532, loss: 0.43419262766838074
2023-01-23 21:58:53.846380: step: 3048/1532, loss: 0.01194723416119814
2023-01-23 21:58:54.660426: step: 3050/1532, loss: 0.03185965493321419
2023-01-23 21:58:55.472218: step: 3052/1532, loss: 0.00019652517221402377
2023-01-23 21:58:56.289210: step: 3054/1532, loss: 0.0003478838480077684
2023-01-23 21:58:57.126136: step: 3056/1532, loss: 0.0001908040721900761
2023-01-23 21:58:57.948305: step: 3058/1532, loss: 0.00013850424147676677
2023-01-23 21:58:58.766274: step: 3060/1532, loss: 0.0005229567177593708
2023-01-23 21:58:59.595196: step: 3062/1532, loss: 0.02243075892329216
2023-01-23 21:59:00.416573: step: 3064/1532, loss: 0.0009529427625238895
2023-01-23 21:59:00.984365: step: 3066/1532, loss: 6.970380491111428e-05
==================================================
Loss: 0.052
--------------------
Dev Chinese: {'event': {'p': 0.6021978021978022, 'r': 0.729693741677763, 'f1': 0.6598434677904876}, 'argument': {'p': 0.6714019218205901, 'r': 0.6664322554858708, 'f1': 0.6689078582179876}, 'combined': 0.44137448079886477, 'epoch': 39}
Test Chinese: {'event': {'p': 0.623972933784437, 'r': 0.7377142857142858, 'f1': 0.6760932181199268}, 'argument': {'p': 0.6929089481752279, 'r': 0.6888913997035434, 'f1': 0.6908943334806873}, 'combined': 0.46710897330377976, 'epoch': 39}
Dev Korean: {'event': {'p': 0.5852631578947368, 'r': 0.7403462050599201, 'f1': 0.6537330981775425}, 'argument': {'p': 0.6645915159587793, 'r': 0.6737483243730047, 'f1': 0.6691385951100997}, 'combined': 0.4374380468914937, 'epoch': 39}
Test Korean: {'event': {'p': 0.6194029850746269, 'r': 0.7588571428571429, 'f1': 0.682074987159733}, 'argument': {'p': 0.6895274559371185, 'r': 0.701836299037377, 'f1': 0.6956274317616332}, 'combined': 0.47447007158677407, 'epoch': 39}
Dev Russian: {'event': {'p': 0.6238425925925926, 'r': 0.7177097203728362, 'f1': 0.6674922600619194}, 'argument': {'p': 0.6698184279121179, 'r': 0.6623036362751084, 'f1': 0.6660398358027979}, 'combined': 0.44457643529127927, 'epoch': 39}
Test Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6954191447086768, 'r': 0.6815690778853384, 'f1': 0.6884244577052545}, 'combined': 0.46227001366810494, 'epoch': 39}
Chinese: {'event': {'p': 0.5316455696202531, 'r': 0.7777777777777778, 'f1': 0.6315789473684209}, 'argument': {'p': 0.6468043822412755, 'r': 0.5505855485194328, 'f1': 0.5948290300968871}, 'combined': 0.37568149269277074, 'epoch': 39}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.6708882134414049, 'r': 0.450453514739229, 'f1': 0.5390042056708724}, 'combined': 0.325261158594492, 'epoch': 39}
Russian: {'event': {'p': 0.53125, 'r': 0.4722222222222222, 'f1': 0.4999999999999999}, 'argument': {'p': 0.5633455274750481, 'r': 0.5774291656619244, 'f1': 0.5703004105302956}, 'combined': 0.2851502052651477, 'epoch': 39}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6098901098901099, 'r': 0.7390146471371505, 'f1': 0.6682721252257676}, 'argument': {'p': 0.6619639597961121, 'r': 0.6663803242424909, 'f1': 0.6641648004456004}, 'combined': 0.4438428226939293, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6230062832286128, 'r': 0.7365714285714285, 'f1': 0.6750458235140089}, 'argument': {'p': 0.67250045002088, 'r': 0.6807604350774312, 'f1': 0.6766052340494917}, 'combined': 0.45673953741282786, 'epoch': 16}
Chinese: {'event': {'p': 0.569620253164557, 'r': 0.8333333333333334, 'f1': 0.6766917293233082}, 'argument': {'p': 0.664119066773934, 'r': 0.6202103681442523, 'f1': 0.6414141414141412}, 'combined': 0.43403964456596017, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5821052631578948, 'r': 0.7363515312916112, 'f1': 0.6502057613168725}, 'argument': {'p': 0.6758296223890353, 'r': 0.6699528430639133, 'f1': 0.672878401330568}, 'combined': 0.437509413210822, 'epoch': 25}
Test for Korean: {'event': {'p': 0.621268656716418, 'r': 0.7611428571428571, 'f1': 0.6841294298921418}, 'argument': {'p': 0.6879208268481474, 'r': 0.6918624021957774, 'f1': 0.6898859846462347}, 'combined': 0.4719713053666074, 'epoch': 25}
Korean: {'event': {'p': 0.660377358490566, 'r': 0.5555555555555556, 'f1': 0.6034482758620691}, 'argument': {'p': 0.7129263470093921, 'r': 0.5194177671068427, 'f1': 0.6009792346690742}, 'combined': 0.36265988298995866, 'epoch': 25}
--------------------
Dev for Russian: {'event': {'p': 0.6273148148148148, 'r': 0.7217043941411452, 'f1': 0.6712074303405573}, 'argument': {'p': 0.643334827065618, 'r': 0.6732125493712872, 'f1': 0.657934665747195}, 'combined': 0.4416106363281482, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6321897073662966, 'r': 0.716, 'f1': 0.6714898177920685}, 'argument': {'p': 0.6632606762497928, 'r': 0.7039912690690404, 'f1': 0.6830192881353655}, 'combined': 0.45864049733848494, 'epoch': 7}
Russian: {'event': {'p': 0.59375, 'r': 0.5277777777777778, 'f1': 0.5588235294117648}, 'argument': {'p': 0.6321972065874505, 'r': 0.6321972065874505, 'f1': 0.6321972065874505}, 'combined': 0.3532866742694577, 'epoch': 7}