Command that produces this log: python train.py --model_name coref --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --accumulate_step 4 --max_epoch 20 --event_hidden_num 500 --p1_data_weight 0.2 --learning_rate 9e-4
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> trainable params:
>>> 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])
>>> type_embedding.weight: torch.Size([123, 100])
>>> trans_rep.weight: torch.Size([1024, 1124])
>>> trans_rep.bias: torch.Size([1024])
>>> coref_type_ffn.weight: torch.Size([3, 4096])
>>> coref_type_ffn.bias: torch.Size([3])
n_trainable_params: 561067023, n_nontrainable_params: 0
----------------------------------------------------------------------------------------------------
******************************
Epoch: 0
command: python train.py --model_name coref --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --accumulate_step 4 --max_epoch 20 --event_hidden_num 500 --p1_data_weight 0.2 --learning_rate 9e-4
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2023-01-24 05:05:46.148205: step: 2116/530, loss: 0.005013451911509037
2023-01-24 05:05:47.176465: step: 2120/530, loss: 0.007228979840874672
==================================================
Loss: 0.022
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3738546380090498, 'r': 0.3135555028462998, 'f1': 0.34106037151702784}, 'combined': 0.25130764217044155, 'stategy': 1, 'epoch': 0}
Test Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.36737558326918535, 'r': 0.3794979673183977, 'f1': 0.373338397064357}, 'combined': 0.28938191542787484, 'stategy': 1, 'epoch': 0}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38570432079081635, 'r': 0.32788526701002985, 'f1': 0.35445238095238096}, 'combined': 0.2611754385964912, 'stategy': 1, 'epoch': 0}
Test Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.35770135689369326, 'r': 0.35375612133971873, 'f1': 0.35571780038596484}, 'combined': 0.2757238452752455, 'stategy': 1, 'epoch': 0}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3831916622413657, 'r': 0.3242950310429774, 'f1': 0.35129184246587686}, 'combined': 0.2588466207643303, 'stategy': 1, 'epoch': 0}
Test Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3533647798208727, 'r': 0.3517408607959607, 'f1': 0.352550950295721}, 'combined': 0.2732691576454871, 'stategy': 1, 'epoch': 0}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 0}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.45588235294117646, 'r': 0.33695652173913043, 'f1': 0.3875}, 'combined': 0.19375, 'stategy': 1, 'epoch': 0}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6096491228070176, 'r': 0.18920145190562612, 'f1': 0.28878116343490307}, 'combined': 0.1925207756232687, 'stategy': 1, 'epoch': 0}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3738546380090498, 'r': 0.3135555028462998, 'f1': 0.34106037151702784}, 'combined': 0.25130764217044155, 'stategy': 1, 'epoch': 0}
Test for Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.36737558326918535, 'r': 0.3794979673183977, 'f1': 0.373338397064357}, 'combined': 0.28938191542787484, 'stategy': 1, 'epoch': 0}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 0}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38570432079081635, 'r': 0.32788526701002985, 'f1': 0.35445238095238096}, 'combined': 0.2611754385964912, 'stategy': 1, 'epoch': 0}
Test for Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.35770135689369326, 'r': 0.35375612133971873, 'f1': 0.35571780038596484}, 'combined': 0.2757238452752455, 'stategy': 1, 'epoch': 0}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.45588235294117646, 'r': 0.33695652173913043, 'f1': 0.3875}, 'combined': 0.19375, 'stategy': 1, 'epoch': 0}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3831916622413657, 'r': 0.3242950310429774, 'f1': 0.35129184246587686}, 'combined': 0.2588466207643303, 'stategy': 1, 'epoch': 0}
Test for Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3533647798208727, 'r': 0.3517408607959607, 'f1': 0.352550950295721}, 'combined': 0.2732691576454871, 'stategy': 1, 'epoch': 0}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6096491228070176, 'r': 0.18920145190562612, 'f1': 0.28878116343490307}, 'combined': 0.1925207756232687, 'stategy': 1, 'epoch': 0}
******************************
Epoch: 1
command: python train.py --model_name coref --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --accumulate_step 4 --max_epoch 20 --event_hidden_num 500 --p1_data_weight 0.2 --learning_rate 9e-4
2023-01-24 05:08:23.823845: step: 4/530, loss: 0.0098522137850523
2023-01-24 05:08:24.865194: step: 8/530, loss: 0.005377455148845911
2023-01-24 05:08:25.897502: step: 12/530, loss: 0.006909853778779507
2023-01-24 05:08:26.918249: step: 16/530, loss: 0.006114135030657053
2023-01-24 05:08:27.947446: step: 20/530, loss: 0.012564241886138916
2023-01-24 05:08:29.007094: step: 24/530, loss: 0.0422721803188324
2023-01-24 05:08:30.041807: step: 28/530, loss: 0.020891595631837845
2023-01-24 05:08:31.073074: step: 32/530, loss: 0.0032217446714639664
2023-01-24 05:08:32.113851: step: 36/530, loss: 0.005921873264014721
2023-01-24 05:08:33.148300: step: 40/530, loss: 0.010434969328343868
2023-01-24 05:08:34.184905: step: 44/530, loss: 0.00887048989534378
2023-01-24 05:08:35.230187: step: 48/530, loss: 0.029717912897467613
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==================================================
Loss: 0.015
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.36731085526315793, 'r': 0.3178249525616698, 'f1': 0.34078077314343846}, 'combined': 0.2511016223162178, 'stategy': 1, 'epoch': 1}
Test Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3751047802521255, 'r': 0.39096398971131463, 'f1': 0.3828702257478941}, 'combined': 0.29677022282851123, 'stategy': 1, 'epoch': 1}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3819074852163088, 'r': 0.33262910002710766, 'f1': 0.35556903796001166}, 'combined': 0.26199823849685067, 'stategy': 1, 'epoch': 1}
Test Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.36785471839114864, 'r': 0.36616420957501283, 'f1': 0.36700751728937253}, 'combined': 0.2844747263199921, 'stategy': 1, 'epoch': 1}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38236684276980326, 'r': 0.33520584698225636, 'f1': 0.35723656493356754}, 'combined': 0.2632269425826287, 'stategy': 1, 'epoch': 1}
Test Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3561820350737081, 'r': 0.35978314020772534, 'f1': 0.35797353136351645}, 'combined': 0.27747230660712763, 'stategy': 1, 'epoch': 1}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 1}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.475, 'r': 0.41304347826086957, 'f1': 0.44186046511627913}, 'combined': 0.22093023255813957, 'stategy': 1, 'epoch': 1}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 1}
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3738546380090498, 'r': 0.3135555028462998, 'f1': 0.34106037151702784}, 'combined': 0.25130764217044155, 'stategy': 1, 'epoch': 0}
Test for Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.36737558326918535, 'r': 0.3794979673183977, 'f1': 0.373338397064357}, 'combined': 0.28938191542787484, 'stategy': 1, 'epoch': 0}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 0}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3819074852163088, 'r': 0.33262910002710766, 'f1': 0.35556903796001166}, 'combined': 0.26199823849685067, 'stategy': 1, 'epoch': 1}
Test for Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.36785471839114864, 'r': 0.36616420957501283, 'f1': 0.36700751728937253}, 'combined': 0.2844747263199921, 'stategy': 1, 'epoch': 1}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.475, 'r': 0.41304347826086957, 'f1': 0.44186046511627913}, 'combined': 0.22093023255813957, 'stategy': 1, 'epoch': 1}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38236684276980326, 'r': 0.33520584698225636, 'f1': 0.35723656493356754}, 'combined': 0.2632269425826287, 'stategy': 1, 'epoch': 1}
Test for Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3561820350737081, 'r': 0.35978314020772534, 'f1': 0.35797353136351645}, 'combined': 0.27747230660712763, 'stategy': 1, 'epoch': 1}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 1}
******************************
Epoch: 2
command: python train.py --model_name coref --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --accumulate_step 4 --max_epoch 20 --event_hidden_num 500 --p1_data_weight 0.2 --learning_rate 9e-4
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2023-01-24 05:29:33.851258: step: 2120/530, loss: 0.028923824429512024
==================================================
Loss: 0.013
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.36750277161862527, 'r': 0.3145042694497154, 'f1': 0.33894427402862987}, 'combined': 0.24974841244214832, 'stategy': 1, 'epoch': 2}
Test Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37477468848365025, 'r': 0.3916533279466088, 'f1': 0.3830281535334026}, 'combined': 0.29689263575316377, 'stategy': 1, 'epoch': 2}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3861756951102589, 'r': 0.32755319869883437, 'f1': 0.3544569521853916}, 'combined': 0.2611788068734464, 'stategy': 1, 'epoch': 2}
Test Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3702915838099576, 'r': 0.3696109007514834, 'f1': 0.36995092917903766}, 'combined': 0.28675622261724454, 'stategy': 1, 'epoch': 2}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3735914626571876, 'r': 0.32538611263690537, 'f1': 0.34782653419807125}, 'combined': 0.2562932357248946, 'stategy': 1, 'epoch': 2}
Test Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3567007815200777, 'r': 0.35932358138419596, 'f1': 0.358007377789382}, 'combined': 0.27749854163578896, 'stategy': 1, 'epoch': 2}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 2}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5125, 'r': 0.44565217391304346, 'f1': 0.47674418604651164}, 'combined': 0.23837209302325582, 'stategy': 1, 'epoch': 2}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 2}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3738546380090498, 'r': 0.3135555028462998, 'f1': 0.34106037151702784}, 'combined': 0.25130764217044155, 'stategy': 1, 'epoch': 0}
Test for Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.36737558326918535, 'r': 0.3794979673183977, 'f1': 0.373338397064357}, 'combined': 0.28938191542787484, 'stategy': 1, 'epoch': 0}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 0}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3861756951102589, 'r': 0.32755319869883437, 'f1': 0.3544569521853916}, 'combined': 0.2611788068734464, 'stategy': 1, 'epoch': 2}
Test for Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3702915838099576, 'r': 0.3696109007514834, 'f1': 0.36995092917903766}, 'combined': 0.28675622261724454, 'stategy': 1, 'epoch': 2}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5125, 'r': 0.44565217391304346, 'f1': 0.47674418604651164}, 'combined': 0.23837209302325582, 'stategy': 1, 'epoch': 2}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38236684276980326, 'r': 0.33520584698225636, 'f1': 0.35723656493356754}, 'combined': 0.2632269425826287, 'stategy': 1, 'epoch': 1}
Test for Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3561820350737081, 'r': 0.35978314020772534, 'f1': 0.35797353136351645}, 'combined': 0.27747230660712763, 'stategy': 1, 'epoch': 1}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 1}
******************************
Epoch: 3
command: python train.py --model_name coref --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --accumulate_step 4 --max_epoch 20 --event_hidden_num 500 --p1_data_weight 0.2 --learning_rate 9e-4
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2023-01-24 05:41:20.742877: step: 2120/530, loss: 0.0015386679442599416
==================================================
Loss: 0.011
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.37004213483146065, 'r': 0.3124644212523719, 'f1': 0.3388245884773662}, 'combined': 0.2496602230885856, 'stategy': 1, 'epoch': 3}
Test Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3778885314389228, 'r': 0.3900448720642558, 'f1': 0.3838704846729175}, 'combined': 0.29754554314360115, 'stategy': 1, 'epoch': 3}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3847625811688312, 'r': 0.3212439007861209, 'f1': 0.35014588565519283}, 'combined': 0.25800223153540525, 'stategy': 1, 'epoch': 3}
Test Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37210117320075786, 'r': 0.3673131066338364, 'f1': 0.3696916373890971}, 'combined': 0.2865552404642762, 'stategy': 1, 'epoch': 3}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38675608320634547, 'r': 0.329513247361763, 'f1': 0.3558472978681334}, 'combined': 0.2622032721133614, 'stategy': 1, 'epoch': 3}
Test Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.36457016286531846, 'r': 0.36322983138419596, 'f1': 0.3638987629337065}, 'combined': 0.2820650698337821, 'stategy': 1, 'epoch': 3}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 3}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5, 'r': 0.41304347826086957, 'f1': 0.45238095238095233}, 'combined': 0.22619047619047616, 'stategy': 1, 'epoch': 3}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 3}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3738546380090498, 'r': 0.3135555028462998, 'f1': 0.34106037151702784}, 'combined': 0.25130764217044155, 'stategy': 1, 'epoch': 0}
Test for Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.36737558326918535, 'r': 0.3794979673183977, 'f1': 0.373338397064357}, 'combined': 0.28938191542787484, 'stategy': 1, 'epoch': 0}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 0}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3861756951102589, 'r': 0.32755319869883437, 'f1': 0.3544569521853916}, 'combined': 0.2611788068734464, 'stategy': 1, 'epoch': 2}
Test for Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3702915838099576, 'r': 0.3696109007514834, 'f1': 0.36995092917903766}, 'combined': 0.28675622261724454, 'stategy': 1, 'epoch': 2}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5125, 'r': 0.44565217391304346, 'f1': 0.47674418604651164}, 'combined': 0.23837209302325582, 'stategy': 1, 'epoch': 2}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38236684276980326, 'r': 0.33520584698225636, 'f1': 0.35723656493356754}, 'combined': 0.2632269425826287, 'stategy': 1, 'epoch': 1}
Test for Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3561820350737081, 'r': 0.35978314020772534, 'f1': 0.35797353136351645}, 'combined': 0.27747230660712763, 'stategy': 1, 'epoch': 1}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 1}
******************************
Epoch: 4
command: python train.py --model_name coref --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --accumulate_step 4 --max_epoch 20 --event_hidden_num 500 --p1_data_weight 0.2 --learning_rate 9e-4
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2023-01-24 05:52:56.024403: step: 2120/530, loss: 0.026345986872911453
==================================================
Loss: 0.015
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.36950383771929823, 'r': 0.319722485768501, 'f1': 0.3428153611393693}, 'combined': 0.2526007924184826, 'stategy': 1, 'epoch': 4}
Test Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37742601309099055, 'r': 0.3923426661819029, 'f1': 0.38473981145192454}, 'combined': 0.2982193753837884, 'stategy': 1, 'epoch': 4}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3811305607769424, 'r': 0.32978280021686096, 'f1': 0.3536023107106525}, 'combined': 0.26054907104995445, 'stategy': 1, 'epoch': 4}
Test Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.36785471839114864, 'r': 0.36616420957501283, 'f1': 0.36700751728937253}, 'combined': 0.2844747263199921, 'stategy': 1, 'epoch': 4}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3753322107351712, 'r': 0.3290388640600552, 'f1': 0.35066426968584247}, 'combined': 0.2583841987158839, 'stategy': 1, 'epoch': 4}
Test Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3599395568707655, 'r': 0.3609320372665489, 'f1': 0.360435113855902}, 'combined': 0.27938032748639297, 'stategy': 1, 'epoch': 4}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 4}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.475, 'r': 0.41304347826086957, 'f1': 0.44186046511627913}, 'combined': 0.22093023255813957, 'stategy': 1, 'epoch': 4}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 4}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.36950383771929823, 'r': 0.319722485768501, 'f1': 0.3428153611393693}, 'combined': 0.2526007924184826, 'stategy': 1, 'epoch': 4}
Test for Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37742601309099055, 'r': 0.3923426661819029, 'f1': 0.38473981145192454}, 'combined': 0.2982193753837884, 'stategy': 1, 'epoch': 4}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 4}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3861756951102589, 'r': 0.32755319869883437, 'f1': 0.3544569521853916}, 'combined': 0.2611788068734464, 'stategy': 1, 'epoch': 2}
Test for Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3702915838099576, 'r': 0.3696109007514834, 'f1': 0.36995092917903766}, 'combined': 0.28675622261724454, 'stategy': 1, 'epoch': 2}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5125, 'r': 0.44565217391304346, 'f1': 0.47674418604651164}, 'combined': 0.23837209302325582, 'stategy': 1, 'epoch': 2}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38236684276980326, 'r': 0.33520584698225636, 'f1': 0.35723656493356754}, 'combined': 0.2632269425826287, 'stategy': 1, 'epoch': 1}
Test for Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3561820350737081, 'r': 0.35978314020772534, 'f1': 0.35797353136351645}, 'combined': 0.27747230660712763, 'stategy': 1, 'epoch': 1}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 1}
******************************
Epoch: 5
command: python train.py --model_name coref --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --accumulate_step 4 --max_epoch 20 --event_hidden_num 500 --p1_data_weight 0.2 --learning_rate 9e-4
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2023-01-24 06:04:42.872622: step: 2120/530, loss: 0.001339518348686397
==================================================
Loss: 0.011
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.37424715909090905, 'r': 0.3124644212523719, 'f1': 0.34057652533609095}, 'combined': 0.2509511239318565, 'stategy': 1, 'epoch': 5}
Test Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3799095807023327, 'r': 0.3907342102995499, 'f1': 0.3852458729550614}, 'combined': 0.2986116335823921, 'stategy': 1, 'epoch': 5}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.39336880300065236, 'r': 0.3269365004066143, 'f1': 0.3570891931902295}, 'combined': 0.26311835287701124, 'stategy': 1, 'epoch': 5}
Test Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3691424037724075, 'r': 0.361338841927954, 'f1': 0.3651989410289029}, 'combined': 0.28307286338125487, 'stategy': 1, 'epoch': 5}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38848653548019935, 'r': 0.329513247361763, 'f1': 0.35657799047155875}, 'combined': 0.2627416771895696, 'stategy': 1, 'epoch': 5}
Test Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.36596083711111593, 'r': 0.3602426990312548, 'f1': 0.3630792557165402}, 'combined': 0.28142985371329915, 'stategy': 1, 'epoch': 5}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 5}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5, 'r': 0.41304347826086957, 'f1': 0.45238095238095233}, 'combined': 0.22619047619047616, 'stategy': 1, 'epoch': 5}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 5}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.36950383771929823, 'r': 0.319722485768501, 'f1': 0.3428153611393693}, 'combined': 0.2526007924184826, 'stategy': 1, 'epoch': 4}
Test for Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37742601309099055, 'r': 0.3923426661819029, 'f1': 0.38473981145192454}, 'combined': 0.2982193753837884, 'stategy': 1, 'epoch': 4}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 4}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3861756951102589, 'r': 0.32755319869883437, 'f1': 0.3544569521853916}, 'combined': 0.2611788068734464, 'stategy': 1, 'epoch': 2}
Test for Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3702915838099576, 'r': 0.3696109007514834, 'f1': 0.36995092917903766}, 'combined': 0.28675622261724454, 'stategy': 1, 'epoch': 2}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5125, 'r': 0.44565217391304346, 'f1': 0.47674418604651164}, 'combined': 0.23837209302325582, 'stategy': 1, 'epoch': 2}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38236684276980326, 'r': 0.33520584698225636, 'f1': 0.35723656493356754}, 'combined': 0.2632269425826287, 'stategy': 1, 'epoch': 1}
Test for Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3561820350737081, 'r': 0.35978314020772534, 'f1': 0.35797353136351645}, 'combined': 0.27747230660712763, 'stategy': 1, 'epoch': 1}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 1}
******************************
Epoch: 6
command: python train.py --model_name coref --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --accumulate_step 4 --max_epoch 20 --event_hidden_num 500 --p1_data_weight 0.2 --learning_rate 9e-4
2023-01-24 06:07:02.319351: step: 4/530, loss: 0.002410094253718853
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2023-01-24 06:07:05.476255: step: 16/530, loss: 0.01219247281551361
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2023-01-24 06:07:08.612371: step: 28/530, loss: 0.003494005184620619
2023-01-24 06:07:09.649759: step: 32/530, loss: 0.01221332885324955
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2023-01-24 06:16:15.608155: step: 2120/530, loss: 0.007919400930404663
==================================================
Loss: 0.010
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.37704166666666666, 'r': 0.32195208728652747, 'f1': 0.3473259979529171}, 'combined': 0.25592441954425466, 'stategy': 1, 'epoch': 6}
Test Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37621134584593835, 'r': 0.3907342102995499, 'f1': 0.3833352757492428}, 'combined': 0.29713069220754706, 'stategy': 1, 'epoch': 6}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3901686065051021, 'r': 0.3316803334236921, 'f1': 0.3585549450549451}, 'combined': 0.26419838056680167, 'stategy': 1, 'epoch': 6}
Test Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.372921689895649, 'r': 0.369151341927954, 'f1': 0.37102693766061334}, 'combined': 0.2875902578996142, 'stategy': 1, 'epoch': 6}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38162687387231753, 'r': 0.33093639726688634, 'f1': 0.3544786206496933}, 'combined': 0.2611947731103003, 'stategy': 1, 'epoch': 6}
Test Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.36231071373713714, 'r': 0.36231071373713714, 'f1': 0.3623107137371372}, 'combined': 0.280834141748403, 'stategy': 1, 'epoch': 6}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 6}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5, 'r': 0.41304347826086957, 'f1': 0.45238095238095233}, 'combined': 0.22619047619047616, 'stategy': 1, 'epoch': 6}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 6}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.37704166666666666, 'r': 0.32195208728652747, 'f1': 0.3473259979529171}, 'combined': 0.25592441954425466, 'stategy': 1, 'epoch': 6}
Test for Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37621134584593835, 'r': 0.3907342102995499, 'f1': 0.3833352757492428}, 'combined': 0.29713069220754706, 'stategy': 1, 'epoch': 6}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 6}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3861756951102589, 'r': 0.32755319869883437, 'f1': 0.3544569521853916}, 'combined': 0.2611788068734464, 'stategy': 1, 'epoch': 2}
Test for Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3702915838099576, 'r': 0.3696109007514834, 'f1': 0.36995092917903766}, 'combined': 0.28675622261724454, 'stategy': 1, 'epoch': 2}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5125, 'r': 0.44565217391304346, 'f1': 0.47674418604651164}, 'combined': 0.23837209302325582, 'stategy': 1, 'epoch': 2}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38236684276980326, 'r': 0.33520584698225636, 'f1': 0.35723656493356754}, 'combined': 0.2632269425826287, 'stategy': 1, 'epoch': 1}
Test for Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3561820350737081, 'r': 0.35978314020772534, 'f1': 0.35797353136351645}, 'combined': 0.27747230660712763, 'stategy': 1, 'epoch': 1}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 1}
******************************
Epoch: 7
command: python train.py --model_name coref --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --accumulate_step 4 --max_epoch 20 --event_hidden_num 500 --p1_data_weight 0.2 --learning_rate 9e-4
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2023-01-24 06:27:58.497487: step: 2120/530, loss: 0.002114777220413089
==================================================
Loss: 0.009
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.371044481981982, 'r': 0.31260673624288426, 'f1': 0.33932801235839344}, 'combined': 0.2500311670009215, 'stategy': 1, 'epoch': 7}
Test Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3773299117490305, 'r': 0.3898150926524911, 'f1': 0.38347090488780317}, 'combined': 0.29723582101351254, 'stategy': 1, 'epoch': 7}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3910125811688312, 'r': 0.3264621171049065, 'f1': 0.3558335795538485}, 'combined': 0.2621931638817831, 'stategy': 1, 'epoch': 7}
Test Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37070452515606517, 'r': 0.3659344301632481, 'f1': 0.3683040333187918}, 'combined': 0.28547968132844154, 'stategy': 1, 'epoch': 7}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38898325469855033, 'r': 0.33141078056859413, 'f1': 0.3578964781960023}, 'combined': 0.2637131944602122, 'stategy': 1, 'epoch': 7}
Test Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.36526204478413765, 'r': 0.36391916961949006, 'f1': 0.3645893706685131}, 'combined': 0.2826003734368379, 'stategy': 1, 'epoch': 7}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4, 'r': 0.2857142857142857, 'f1': 0.3333333333333333}, 'combined': 0.2222222222222222, 'stategy': 1, 'epoch': 7}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5, 'r': 0.41304347826086957, 'f1': 0.45238095238095233}, 'combined': 0.22619047619047616, 'stategy': 1, 'epoch': 7}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 7}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.37704166666666666, 'r': 0.32195208728652747, 'f1': 0.3473259979529171}, 'combined': 0.25592441954425466, 'stategy': 1, 'epoch': 6}
Test for Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37621134584593835, 'r': 0.3907342102995499, 'f1': 0.3833352757492428}, 'combined': 0.29713069220754706, 'stategy': 1, 'epoch': 6}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 6}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3861756951102589, 'r': 0.32755319869883437, 'f1': 0.3544569521853916}, 'combined': 0.2611788068734464, 'stategy': 1, 'epoch': 2}
Test for Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3702915838099576, 'r': 0.3696109007514834, 'f1': 0.36995092917903766}, 'combined': 0.28675622261724454, 'stategy': 1, 'epoch': 2}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5125, 'r': 0.44565217391304346, 'f1': 0.47674418604651164}, 'combined': 0.23837209302325582, 'stategy': 1, 'epoch': 2}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38898325469855033, 'r': 0.33141078056859413, 'f1': 0.3578964781960023}, 'combined': 0.2637131944602122, 'stategy': 1, 'epoch': 7}
Test for Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.36526204478413765, 'r': 0.36391916961949006, 'f1': 0.3645893706685131}, 'combined': 0.2826003734368379, 'stategy': 1, 'epoch': 7}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 7}
******************************
Epoch: 8
command: python train.py --model_name coref --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --accumulate_step 4 --max_epoch 20 --event_hidden_num 500 --p1_data_weight 0.2 --learning_rate 9e-4
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2023-01-24 06:39:38.675766: step: 2120/530, loss: 0.0041249594651162624
==================================================
Loss: 0.009
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.37509743875278395, 'r': 0.3195801707779886, 'f1': 0.3451203893442623}, 'combined': 0.25429923425366696, 'stategy': 1, 'epoch': 8}
Test Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37477309194500474, 'r': 0.38889597500543227, 'f1': 0.38170394299134897}, 'combined': 0.295866214184682, 'stategy': 1, 'epoch': 8}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3953728281853282, 'r': 0.33310348332881545, 'f1': 0.3615767985876122}, 'combined': 0.266425009485609, 'stategy': 1, 'epoch': 8}
Test Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37187421934722487, 'r': 0.36777266545736575, 'f1': 0.3698120702565748}, 'combined': 0.28664859034241685, 'stategy': 1, 'epoch': 8}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38720415311180817, 'r': 0.33283393047371745, 'f1': 0.35796628848907985}, 'combined': 0.26376463362353253, 'stategy': 1, 'epoch': 8}
Test Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.36421427197230394, 'r': 0.3625404931489018, 'f1': 0.3633754551322019}, 'combined': 0.28165944369098905, 'stategy': 1, 'epoch': 8}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 8}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5, 'r': 0.41304347826086957, 'f1': 0.45238095238095233}, 'combined': 0.22619047619047616, 'stategy': 1, 'epoch': 8}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 8}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.37704166666666666, 'r': 0.32195208728652747, 'f1': 0.3473259979529171}, 'combined': 0.25592441954425466, 'stategy': 1, 'epoch': 6}
Test for Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37621134584593835, 'r': 0.3907342102995499, 'f1': 0.3833352757492428}, 'combined': 0.29713069220754706, 'stategy': 1, 'epoch': 6}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 6}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3861756951102589, 'r': 0.32755319869883437, 'f1': 0.3544569521853916}, 'combined': 0.2611788068734464, 'stategy': 1, 'epoch': 2}
Test for Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3702915838099576, 'r': 0.3696109007514834, 'f1': 0.36995092917903766}, 'combined': 0.28675622261724454, 'stategy': 1, 'epoch': 2}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5125, 'r': 0.44565217391304346, 'f1': 0.47674418604651164}, 'combined': 0.23837209302325582, 'stategy': 1, 'epoch': 2}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38720415311180817, 'r': 0.33283393047371745, 'f1': 0.35796628848907985}, 'combined': 0.26376463362353253, 'stategy': 1, 'epoch': 8}
Test for Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.36421427197230394, 'r': 0.3625404931489018, 'f1': 0.3633754551322019}, 'combined': 0.28165944369098905, 'stategy': 1, 'epoch': 8}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 8}
******************************
Epoch: 9
command: python train.py --model_name coref --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --accumulate_step 4 --max_epoch 20 --event_hidden_num 500 --p1_data_weight 0.2 --learning_rate 9e-4
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2023-01-24 06:51:25.422395: step: 2116/530, loss: 0.00747764203697443
2023-01-24 06:51:26.461535: step: 2120/530, loss: 0.003745673457160592
==================================================
Loss: 0.007
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.373875, 'r': 0.3192481024667932, 'f1': 0.34440890481064484}, 'combined': 0.2537749824920541, 'stategy': 1, 'epoch': 9}
Test Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37466326926317167, 'r': 0.3898150926524911, 'f1': 0.38208902775307235}, 'combined': 0.2961647009377882, 'stategy': 1, 'epoch': 9}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3820300126702566, 'r': 0.3269365004066143, 'f1': 0.3523426088226702}, 'combined': 0.2596208696588096, 'stategy': 1, 'epoch': 9}
Test Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37245743734225994, 'r': 0.3686917831044246, 'f1': 0.3705650438961792}, 'combined': 0.28723223498172745, 'stategy': 1, 'epoch': 9}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3813119159992372, 'r': 0.33283393047371745, 'f1': 0.3554275204856111}, 'combined': 0.26189396246308183, 'stategy': 1, 'epoch': 9}
Test Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.35810637630247955, 'r': 0.35909380197243124, 'f1': 0.3585994094043186}, 'combined': 0.2779574369545436, 'stategy': 1, 'epoch': 9}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 9}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.475, 'r': 0.41304347826086957, 'f1': 0.44186046511627913}, 'combined': 0.22093023255813957, 'stategy': 1, 'epoch': 9}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 9}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.37704166666666666, 'r': 0.32195208728652747, 'f1': 0.3473259979529171}, 'combined': 0.25592441954425466, 'stategy': 1, 'epoch': 6}
Test for Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37621134584593835, 'r': 0.3907342102995499, 'f1': 0.3833352757492428}, 'combined': 0.29713069220754706, 'stategy': 1, 'epoch': 6}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 6}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3861756951102589, 'r': 0.32755319869883437, 'f1': 0.3544569521853916}, 'combined': 0.2611788068734464, 'stategy': 1, 'epoch': 2}
Test for Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3702915838099576, 'r': 0.3696109007514834, 'f1': 0.36995092917903766}, 'combined': 0.28675622261724454, 'stategy': 1, 'epoch': 2}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5125, 'r': 0.44565217391304346, 'f1': 0.47674418604651164}, 'combined': 0.23837209302325582, 'stategy': 1, 'epoch': 2}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38720415311180817, 'r': 0.33283393047371745, 'f1': 0.35796628848907985}, 'combined': 0.26376463362353253, 'stategy': 1, 'epoch': 8}
Test for Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.36421427197230394, 'r': 0.3625404931489018, 'f1': 0.3633754551322019}, 'combined': 0.28165944369098905, 'stategy': 1, 'epoch': 8}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 8}
******************************
Epoch: 10
command: python train.py --model_name coref --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --accumulate_step 4 --max_epoch 20 --event_hidden_num 500 --p1_data_weight 0.2 --learning_rate 9e-4
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2023-01-24 07:03:05.261745: step: 2120/530, loss: 0.002706417115405202
==================================================
Loss: 0.006
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.37360033259423503, 'r': 0.319722485768501, 'f1': 0.34456799591002046}, 'combined': 0.25389220751264663, 'stategy': 1, 'epoch': 10}
Test Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3764300537154685, 'r': 0.3916533279466088, 'f1': 0.38389082955487414}, 'combined': 0.29756131286071585, 'stategy': 1, 'epoch': 10}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38841283157558326, 'r': 0.3294507319056655, 'f1': 0.35651034027574074}, 'combined': 0.2626918296768616, 'stategy': 1, 'epoch': 10}
Test Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3730395733001985, 'r': 0.3696109007514834, 'f1': 0.37131732226926495}, 'combined': 0.2878153407063202, 'stategy': 1, 'epoch': 10}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38179098765787556, 'r': 0.3310787122573987, 'f1': 0.3546310596740836}, 'combined': 0.26130709660195633, 'stategy': 1, 'epoch': 10}
Test Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.363814560648306, 'r': 0.3641489490312548, 'f1': 0.3639816780395087}, 'combined': 0.28212933895885367, 'stategy': 1, 'epoch': 10}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 10}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5, 'r': 0.41304347826086957, 'f1': 0.45238095238095233}, 'combined': 0.22619047619047616, 'stategy': 1, 'epoch': 10}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 10}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.37704166666666666, 'r': 0.32195208728652747, 'f1': 0.3473259979529171}, 'combined': 0.25592441954425466, 'stategy': 1, 'epoch': 6}
Test for Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37621134584593835, 'r': 0.3907342102995499, 'f1': 0.3833352757492428}, 'combined': 0.29713069220754706, 'stategy': 1, 'epoch': 6}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 6}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3861756951102589, 'r': 0.32755319869883437, 'f1': 0.3544569521853916}, 'combined': 0.2611788068734464, 'stategy': 1, 'epoch': 2}
Test for Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3702915838099576, 'r': 0.3696109007514834, 'f1': 0.36995092917903766}, 'combined': 0.28675622261724454, 'stategy': 1, 'epoch': 2}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5125, 'r': 0.44565217391304346, 'f1': 0.47674418604651164}, 'combined': 0.23837209302325582, 'stategy': 1, 'epoch': 2}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38720415311180817, 'r': 0.33283393047371745, 'f1': 0.35796628848907985}, 'combined': 0.26376463362353253, 'stategy': 1, 'epoch': 8}
Test for Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.36421427197230394, 'r': 0.3625404931489018, 'f1': 0.3633754551322019}, 'combined': 0.28165944369098905, 'stategy': 1, 'epoch': 8}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 8}
******************************
Epoch: 11
command: python train.py --model_name coref --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --accumulate_step 4 --max_epoch 20 --event_hidden_num 500 --p1_data_weight 0.2 --learning_rate 9e-4
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2023-01-24 07:14:44.776149: step: 2120/530, loss: 0.00868659932166338
==================================================
Loss: 0.005
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.37029128959276014, 'r': 0.3105668880455408, 'f1': 0.3378095975232198}, 'combined': 0.24891233501710933, 'stategy': 1, 'epoch': 11}
Test Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3757979351116226, 'r': 0.389614035667197, 'f1': 0.3825812913410743}, 'combined': 0.296546264101694, 'stategy': 1, 'epoch': 11}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3907643182557762, 'r': 0.32551335050149094, 'f1': 0.3551667406092872}, 'combined': 0.2617018088700011, 'stategy': 1, 'epoch': 11}
Test Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37268956361895444, 'r': 0.3689215625161893, 'f1': 0.37079599077839626}, 'combined': 0.2874112464406708, 'stategy': 1, 'epoch': 11}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3806063888240557, 'r': 0.3257181809481008, 'f1': 0.35102961423241125}, 'combined': 0.25865339996072406, 'stategy': 1, 'epoch': 11}
Test Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3628740170616423, 'r': 0.3625404931489018, 'f1': 0.36270717843310823}, 'combined': 0.28114144931178725, 'stategy': 1, 'epoch': 11}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 11}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5, 'r': 0.41304347826086957, 'f1': 0.45238095238095233}, 'combined': 0.22619047619047616, 'stategy': 1, 'epoch': 11}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 11}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.37704166666666666, 'r': 0.32195208728652747, 'f1': 0.3473259979529171}, 'combined': 0.25592441954425466, 'stategy': 1, 'epoch': 6}
Test for Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37621134584593835, 'r': 0.3907342102995499, 'f1': 0.3833352757492428}, 'combined': 0.29713069220754706, 'stategy': 1, 'epoch': 6}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 6}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3861756951102589, 'r': 0.32755319869883437, 'f1': 0.3544569521853916}, 'combined': 0.2611788068734464, 'stategy': 1, 'epoch': 2}
Test for Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3702915838099576, 'r': 0.3696109007514834, 'f1': 0.36995092917903766}, 'combined': 0.28675622261724454, 'stategy': 1, 'epoch': 2}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5125, 'r': 0.44565217391304346, 'f1': 0.47674418604651164}, 'combined': 0.23837209302325582, 'stategy': 1, 'epoch': 2}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38720415311180817, 'r': 0.33283393047371745, 'f1': 0.35796628848907985}, 'combined': 0.26376463362353253, 'stategy': 1, 'epoch': 8}
Test for Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.36421427197230394, 'r': 0.3625404931489018, 'f1': 0.3633754551322019}, 'combined': 0.28165944369098905, 'stategy': 1, 'epoch': 8}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 8}
******************************
Epoch: 12
command: python train.py --model_name coref --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --accumulate_step 4 --max_epoch 20 --event_hidden_num 500 --p1_data_weight 0.2 --learning_rate 9e-4
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2023-01-24 07:25:56.103220: step: 2016/530, loss: 0.003949727397412062
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2023-01-24 07:26:21.102260: step: 2112/530, loss: 0.0022559345234185457
2023-01-24 07:26:22.157045: step: 2116/530, loss: 0.0004722154699265957
2023-01-24 07:26:23.195315: step: 2120/530, loss: 0.0026975416112691164
==================================================
Loss: 0.004
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.37786862527716186, 'r': 0.32337523719165084, 'f1': 0.3485046012269939}, 'combined': 0.2567928640619955, 'stategy': 1, 'epoch': 12}
Test Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37377987703702326, 'r': 0.38889597500543227, 'f1': 0.3811881268521715}, 'combined': 0.2954663949763244, 'stategy': 1, 'epoch': 12}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.39184271364795925, 'r': 0.33310348332881545, 'f1': 0.3600934065934066}, 'combined': 0.265331983805668, 'stategy': 1, 'epoch': 12}
Test Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3731503814117339, 'r': 0.3686917831044246, 'f1': 0.3709076837888247}, 'combined': 0.2874978218841608, 'stategy': 1, 'epoch': 12}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38265397258058376, 'r': 0.33473146368054857, 'f1': 0.3570920675296541}, 'combined': 0.26312047081132406, 'stategy': 1, 'epoch': 12}
Test Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.36080409985833045, 'r': 0.3604724784430195, 'f1': 0.36063821291586684}, 'combined': 0.27953775355201166, 'stategy': 1, 'epoch': 12}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 12}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5, 'r': 0.41304347826086957, 'f1': 0.45238095238095233}, 'combined': 0.22619047619047616, 'stategy': 1, 'epoch': 12}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 12}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.37786862527716186, 'r': 0.32337523719165084, 'f1': 0.3485046012269939}, 'combined': 0.2567928640619955, 'stategy': 1, 'epoch': 12}
Test for Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37377987703702326, 'r': 0.38889597500543227, 'f1': 0.3811881268521715}, 'combined': 0.2954663949763244, 'stategy': 1, 'epoch': 12}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 12}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3861756951102589, 'r': 0.32755319869883437, 'f1': 0.3544569521853916}, 'combined': 0.2611788068734464, 'stategy': 1, 'epoch': 2}
Test for Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3702915838099576, 'r': 0.3696109007514834, 'f1': 0.36995092917903766}, 'combined': 0.28675622261724454, 'stategy': 1, 'epoch': 2}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5125, 'r': 0.44565217391304346, 'f1': 0.47674418604651164}, 'combined': 0.23837209302325582, 'stategy': 1, 'epoch': 2}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38720415311180817, 'r': 0.33283393047371745, 'f1': 0.35796628848907985}, 'combined': 0.26376463362353253, 'stategy': 1, 'epoch': 8}
Test for Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.36421427197230394, 'r': 0.3625404931489018, 'f1': 0.3633754551322019}, 'combined': 0.28165944369098905, 'stategy': 1, 'epoch': 8}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 8}
******************************
Epoch: 13
command: python train.py --model_name coref --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --accumulate_step 4 --max_epoch 20 --event_hidden_num 500 --p1_data_weight 0.2 --learning_rate 9e-4
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2023-01-24 07:38:04.468727: step: 2120/530, loss: 0.00022283566067926586
==================================================
Loss: 0.005
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3701296909492274, 'r': 0.31815702087286524, 'f1': 0.34218112244897964}, 'combined': 0.25213345864661657, 'stategy': 1, 'epoch': 13}
Test Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3768783872392822, 'r': 0.3907342102995499, 'f1': 0.38368124621472044}, 'combined': 0.2973988607023192, 'stategy': 1, 'epoch': 13}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3936708108964207, 'r': 0.3368985497424777, 'f1': 0.3630788051416886}, 'combined': 0.2675317511570337, 'stategy': 1, 'epoch': 13}
Test Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3717670621108563, 'r': 0.3686917831044246, 'f1': 0.37022303647218635}, 'combined': 0.286967138318154, 'stategy': 1, 'epoch': 13}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38297703353635176, 'r': 0.33283393047371745, 'f1': 0.35614920073025197}, 'combined': 0.26242572685386983, 'stategy': 1, 'epoch': 13}
Test Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3629184752942302, 'r': 0.36391916961949006, 'f1': 0.3634181335897248}, 'combined': 0.281692524600648, 'stategy': 1, 'epoch': 13}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 13}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5, 'r': 0.41304347826086957, 'f1': 0.45238095238095233}, 'combined': 0.22619047619047616, 'stategy': 1, 'epoch': 13}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 13}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.37786862527716186, 'r': 0.32337523719165084, 'f1': 0.3485046012269939}, 'combined': 0.2567928640619955, 'stategy': 1, 'epoch': 12}
Test for Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37377987703702326, 'r': 0.38889597500543227, 'f1': 0.3811881268521715}, 'combined': 0.2954663949763244, 'stategy': 1, 'epoch': 12}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 12}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3861756951102589, 'r': 0.32755319869883437, 'f1': 0.3544569521853916}, 'combined': 0.2611788068734464, 'stategy': 1, 'epoch': 2}
Test for Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3702915838099576, 'r': 0.3696109007514834, 'f1': 0.36995092917903766}, 'combined': 0.28675622261724454, 'stategy': 1, 'epoch': 2}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5125, 'r': 0.44565217391304346, 'f1': 0.47674418604651164}, 'combined': 0.23837209302325582, 'stategy': 1, 'epoch': 2}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38720415311180817, 'r': 0.33283393047371745, 'f1': 0.35796628848907985}, 'combined': 0.26376463362353253, 'stategy': 1, 'epoch': 8}
Test for Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.36421427197230394, 'r': 0.3625404931489018, 'f1': 0.3633754551322019}, 'combined': 0.28165944369098905, 'stategy': 1, 'epoch': 8}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 8}
******************************
Epoch: 14
command: python train.py --model_name coref --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --accumulate_step 4 --max_epoch 20 --event_hidden_num 500 --p1_data_weight 0.2 --learning_rate 9e-4
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2023-01-24 07:49:37.214389: step: 2108/530, loss: 0.006133155431598425
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2023-01-24 07:49:39.322471: step: 2116/530, loss: 0.0026042740792036057
2023-01-24 07:49:40.366649: step: 2120/530, loss: 0.00734981382265687
==================================================
Loss: 0.005
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.37371404867256636, 'r': 0.32052893738140414, 'f1': 0.34508426966292133}, 'combined': 0.25427261975162624, 'stategy': 1, 'epoch': 14}
Test Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37292775203343065, 'r': 0.38869491802013817, 'f1': 0.3806481285381731}, 'combined': 0.2950478316898758, 'stategy': 1, 'epoch': 14}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3856567460317461, 'r': 0.32930841691515317, 'f1': 0.35526209972218165}, 'combined': 0.26177207347950227, 'stategy': 1, 'epoch': 14}
Test Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3726981128747585, 'r': 0.37030023898677755, 'f1': 0.3714953066091415}, 'combined': 0.28795329985971735, 'stategy': 1, 'epoch': 14}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3783300247486909, 'r': 0.329513247361763, 'f1': 0.3522382989039536}, 'combined': 0.2595440097187026, 'stategy': 1, 'epoch': 14}
Test Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3599945721881326, 'r': 0.36231071373713714, 'f1': 0.36114892949702726}, 'combined': 0.2799336199929111, 'stategy': 1, 'epoch': 14}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 14}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.475, 'r': 0.41304347826086957, 'f1': 0.44186046511627913}, 'combined': 0.22093023255813957, 'stategy': 1, 'epoch': 14}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 14}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.37786862527716186, 'r': 0.32337523719165084, 'f1': 0.3485046012269939}, 'combined': 0.2567928640619955, 'stategy': 1, 'epoch': 12}
Test for Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37377987703702326, 'r': 0.38889597500543227, 'f1': 0.3811881268521715}, 'combined': 0.2954663949763244, 'stategy': 1, 'epoch': 12}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 12}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3861756951102589, 'r': 0.32755319869883437, 'f1': 0.3544569521853916}, 'combined': 0.2611788068734464, 'stategy': 1, 'epoch': 2}
Test for Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3702915838099576, 'r': 0.3696109007514834, 'f1': 0.36995092917903766}, 'combined': 0.28675622261724454, 'stategy': 1, 'epoch': 2}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5125, 'r': 0.44565217391304346, 'f1': 0.47674418604651164}, 'combined': 0.23837209302325582, 'stategy': 1, 'epoch': 2}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38720415311180817, 'r': 0.33283393047371745, 'f1': 0.35796628848907985}, 'combined': 0.26376463362353253, 'stategy': 1, 'epoch': 8}
Test for Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.36421427197230394, 'r': 0.3625404931489018, 'f1': 0.3633754551322019}, 'combined': 0.28165944369098905, 'stategy': 1, 'epoch': 8}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 8}
******************************
Epoch: 15
command: python train.py --model_name coref --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --accumulate_step 4 --max_epoch 20 --event_hidden_num 500 --p1_data_weight 0.2 --learning_rate 9e-4
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2023-01-24 08:01:15.889968: step: 2116/530, loss: 0.003660043003037572
2023-01-24 08:01:16.932592: step: 2120/530, loss: 0.0012359905522316694
==================================================
Loss: 0.004
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3781810538116592, 'r': 0.32005455407969635, 'f1': 0.34669835560123324}, 'combined': 0.25546194623248764, 'stategy': 1, 'epoch': 15}
Test Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.375326390093726, 'r': 0.3898150926524911, 'f1': 0.382433562494058}, 'combined': 0.2964317565743416, 'stategy': 1, 'epoch': 15}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3899899678972713, 'r': 0.32930841691515317, 'f1': 0.35708957965902416}, 'combined': 0.26311863764349147, 'stategy': 1, 'epoch': 15}
Test Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3731503814117339, 'r': 0.3686917831044246, 'f1': 0.3709076837888247}, 'combined': 0.2874978218841608, 'stategy': 1, 'epoch': 15}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3851604396498889, 'r': 0.33473146368054857, 'f1': 0.3581796575830439}, 'combined': 0.26392185295592707, 'stategy': 1, 'epoch': 15}
Test Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3640887768447368, 'r': 0.3620809343253724, 'f1': 0.3630820797659034}, 'combined': 0.28143204268936056, 'stategy': 1, 'epoch': 15}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 15}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5, 'r': 0.41304347826086957, 'f1': 0.45238095238095233}, 'combined': 0.22619047619047616, 'stategy': 1, 'epoch': 15}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 15}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.37786862527716186, 'r': 0.32337523719165084, 'f1': 0.3485046012269939}, 'combined': 0.2567928640619955, 'stategy': 1, 'epoch': 12}
Test for Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37377987703702326, 'r': 0.38889597500543227, 'f1': 0.3811881268521715}, 'combined': 0.2954663949763244, 'stategy': 1, 'epoch': 12}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 12}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3861756951102589, 'r': 0.32755319869883437, 'f1': 0.3544569521853916}, 'combined': 0.2611788068734464, 'stategy': 1, 'epoch': 2}
Test for Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3702915838099576, 'r': 0.3696109007514834, 'f1': 0.36995092917903766}, 'combined': 0.28675622261724454, 'stategy': 1, 'epoch': 2}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5125, 'r': 0.44565217391304346, 'f1': 0.47674418604651164}, 'combined': 0.23837209302325582, 'stategy': 1, 'epoch': 2}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3851604396498889, 'r': 0.33473146368054857, 'f1': 0.3581796575830439}, 'combined': 0.26392185295592707, 'stategy': 1, 'epoch': 15}
Test for Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3640887768447368, 'r': 0.3620809343253724, 'f1': 0.3630820797659034}, 'combined': 0.28143204268936056, 'stategy': 1, 'epoch': 15}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 15}
******************************
Epoch: 16
command: python train.py --model_name coref --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --accumulate_step 4 --max_epoch 20 --event_hidden_num 500 --p1_data_weight 0.2 --learning_rate 9e-4
2023-01-24 08:03:48.912055: step: 4/530, loss: 9.7860254754778e-05
2023-01-24 08:03:49.964465: step: 8/530, loss: 0.00016962077643256634
2023-01-24 08:03:51.010700: step: 12/530, loss: 0.00167995342053473
2023-01-24 08:03:52.041530: step: 16/530, loss: 0.0009895809926092625
2023-01-24 08:03:53.074076: step: 20/530, loss: 0.0008334103040397167
2023-01-24 08:03:54.120938: step: 24/530, loss: 0.0017199110006913543
2023-01-24 08:03:55.159042: step: 28/530, loss: 0.0062159881927073
2023-01-24 08:03:56.239103: step: 32/530, loss: 0.06644850969314575
2023-01-24 08:03:57.273012: step: 36/530, loss: 0.0005407019634731114
2023-01-24 08:03:58.293488: step: 40/530, loss: 0.0012492379173636436
2023-01-24 08:03:59.322730: step: 44/530, loss: 0.0001143816189141944
2023-01-24 08:04:00.366365: step: 48/530, loss: 0.002293126657605171
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2023-01-24 08:04:10.803513: step: 88/530, loss: 0.012221149168908596
2023-01-24 08:04:11.857757: step: 92/530, loss: 3.227633351343684e-05
2023-01-24 08:04:12.908798: step: 96/530, loss: 0.0001579405361553654
2023-01-24 08:04:13.955444: step: 100/530, loss: 0.00021769414888694882
2023-01-24 08:04:15.027153: step: 104/530, loss: 0.01837671548128128
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2023-01-24 08:12:58.972465: step: 2112/530, loss: 0.004905502311885357
2023-01-24 08:13:00.009287: step: 2116/530, loss: 2.134965507138986e-05
2023-01-24 08:13:01.039666: step: 2120/530, loss: 0.0008202884346246719
==================================================
Loss: 0.004
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.37360033259423503, 'r': 0.319722485768501, 'f1': 0.34456799591002046}, 'combined': 0.25389220751264663, 'stategy': 1, 'epoch': 16}
Test Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3766596987619097, 'r': 0.3898150926524911, 'f1': 0.38312449937299936}, 'combined': 0.2969673153034732, 'stategy': 1, 'epoch': 16}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3915268055997455, 'r': 0.3335778666305232, 'f1': 0.36023675351288054}, 'combined': 0.26543760785159615, 'stategy': 1, 'epoch': 16}
Test Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3717670621108563, 'r': 0.3686917831044246, 'f1': 0.37022303647218635}, 'combined': 0.286967138318154, 'stategy': 1, 'epoch': 16}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38297703353635176, 'r': 0.33283393047371745, 'f1': 0.35614920073025197}, 'combined': 0.26242572685386983, 'stategy': 1, 'epoch': 16}
Test Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3618026351900417, 'r': 0.3604724784430195, 'f1': 0.3611363319944799}, 'combined': 0.2799238554215586, 'stategy': 1, 'epoch': 16}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 16}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5, 'r': 0.41304347826086957, 'f1': 0.45238095238095233}, 'combined': 0.22619047619047616, 'stategy': 1, 'epoch': 16}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 16}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.37786862527716186, 'r': 0.32337523719165084, 'f1': 0.3485046012269939}, 'combined': 0.2567928640619955, 'stategy': 1, 'epoch': 12}
Test for Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37377987703702326, 'r': 0.38889597500543227, 'f1': 0.3811881268521715}, 'combined': 0.2954663949763244, 'stategy': 1, 'epoch': 12}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 12}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3861756951102589, 'r': 0.32755319869883437, 'f1': 0.3544569521853916}, 'combined': 0.2611788068734464, 'stategy': 1, 'epoch': 2}
Test for Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3702915838099576, 'r': 0.3696109007514834, 'f1': 0.36995092917903766}, 'combined': 0.28675622261724454, 'stategy': 1, 'epoch': 2}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5125, 'r': 0.44565217391304346, 'f1': 0.47674418604651164}, 'combined': 0.23837209302325582, 'stategy': 1, 'epoch': 2}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3851604396498889, 'r': 0.33473146368054857, 'f1': 0.3581796575830439}, 'combined': 0.26392185295592707, 'stategy': 1, 'epoch': 15}
Test for Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3640887768447368, 'r': 0.3620809343253724, 'f1': 0.3630820797659034}, 'combined': 0.28143204268936056, 'stategy': 1, 'epoch': 15}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 15}
******************************
Epoch: 17
command: python train.py --model_name coref --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --accumulate_step 4 --max_epoch 20 --event_hidden_num 500 --p1_data_weight 0.2 --learning_rate 9e-4
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2023-01-24 08:15:32.114786: step: 32/530, loss: 0.0004857582098338753
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2023-01-24 08:24:37.019288: step: 2120/530, loss: 0.030014971271157265
==================================================
Loss: 0.003
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3716527777777778, 'r': 0.3173505692599621, 'f1': 0.34236182190378706}, 'combined': 0.25226660561331676, 'stategy': 1, 'epoch': 17}
Test Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3712566585803961, 'r': 0.38797685735837345, 'f1': 0.379432647915425}, 'combined': 0.29410568881482707, 'stategy': 1, 'epoch': 17}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3843719903691814, 'r': 0.3245645838980754, 'f1': 0.3519455467372134}, 'combined': 0.2593282975958414, 'stategy': 1, 'epoch': 17}
Test Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3711842857306252, 'r': 0.36777266545736575, 'f1': 0.3694706002009362}, 'combined': 0.2863839102035965, 'stategy': 1, 'epoch': 17}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3802940118053945, 'r': 0.3276157141549319, 'f1': 0.3519948651572867}, 'combined': 0.2593646374843165, 'stategy': 1, 'epoch': 17}
Test Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.35948126172869405, 'r': 0.3604724784430195, 'f1': 0.35997618774300616}, 'combined': 0.2790246048534306, 'stategy': 1, 'epoch': 17}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 17}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.475, 'r': 0.41304347826086957, 'f1': 0.44186046511627913}, 'combined': 0.22093023255813957, 'stategy': 1, 'epoch': 17}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 17}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.37786862527716186, 'r': 0.32337523719165084, 'f1': 0.3485046012269939}, 'combined': 0.2567928640619955, 'stategy': 1, 'epoch': 12}
Test for Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37377987703702326, 'r': 0.38889597500543227, 'f1': 0.3811881268521715}, 'combined': 0.2954663949763244, 'stategy': 1, 'epoch': 12}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 12}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3861756951102589, 'r': 0.32755319869883437, 'f1': 0.3544569521853916}, 'combined': 0.2611788068734464, 'stategy': 1, 'epoch': 2}
Test for Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3702915838099576, 'r': 0.3696109007514834, 'f1': 0.36995092917903766}, 'combined': 0.28675622261724454, 'stategy': 1, 'epoch': 2}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5125, 'r': 0.44565217391304346, 'f1': 0.47674418604651164}, 'combined': 0.23837209302325582, 'stategy': 1, 'epoch': 2}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3851604396498889, 'r': 0.33473146368054857, 'f1': 0.3581796575830439}, 'combined': 0.26392185295592707, 'stategy': 1, 'epoch': 15}
Test for Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3640887768447368, 'r': 0.3620809343253724, 'f1': 0.3630820797659034}, 'combined': 0.28143204268936056, 'stategy': 1, 'epoch': 15}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 15}
******************************
Epoch: 18
command: python train.py --model_name coref --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --accumulate_step 4 --max_epoch 20 --event_hidden_num 500 --p1_data_weight 0.2 --learning_rate 9e-4
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2023-01-24 08:36:13.707868: step: 2120/530, loss: 3.47580571542494e-05
==================================================
Loss: 0.003
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3699861111111111, 'r': 0.3159274193548387, 'f1': 0.3408265097236438}, 'combined': 0.25113532295426383, 'stategy': 1, 'epoch': 18}
Test Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.378100107192467, 'r': 0.3916533279466088, 'f1': 0.384757400276217}, 'combined': 0.29823300882654147, 'stategy': 1, 'epoch': 18}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3906499680409077, 'r': 0.33134826511249665, 'f1': 0.3585637283660898}, 'combined': 0.2642048524802767, 'stategy': 1, 'epoch': 18}
Test Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3706004271287038, 'r': 0.36685354781030693, 'f1': 0.3687174688384424}, 'combined': 0.2858001433101803, 'stategy': 1, 'epoch': 18}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3796028038504357, 'r': 0.3291811790505676, 'f1': 0.3525985393488803}, 'combined': 0.2598094500465434, 'stategy': 1, 'epoch': 18}
Test Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.36262125027281583, 'r': 0.361621375501843, 'f1': 0.362120622683852}, 'combined': 0.2806867984439427, 'stategy': 1, 'epoch': 18}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 18}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.475, 'r': 0.41304347826086957, 'f1': 0.44186046511627913}, 'combined': 0.22093023255813957, 'stategy': 1, 'epoch': 18}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 18}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.37786862527716186, 'r': 0.32337523719165084, 'f1': 0.3485046012269939}, 'combined': 0.2567928640619955, 'stategy': 1, 'epoch': 12}
Test for Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37377987703702326, 'r': 0.38889597500543227, 'f1': 0.3811881268521715}, 'combined': 0.2954663949763244, 'stategy': 1, 'epoch': 12}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 12}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3861756951102589, 'r': 0.32755319869883437, 'f1': 0.3544569521853916}, 'combined': 0.2611788068734464, 'stategy': 1, 'epoch': 2}
Test for Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3702915838099576, 'r': 0.3696109007514834, 'f1': 0.36995092917903766}, 'combined': 0.28675622261724454, 'stategy': 1, 'epoch': 2}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5125, 'r': 0.44565217391304346, 'f1': 0.47674418604651164}, 'combined': 0.23837209302325582, 'stategy': 1, 'epoch': 2}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3851604396498889, 'r': 0.33473146368054857, 'f1': 0.3581796575830439}, 'combined': 0.26392185295592707, 'stategy': 1, 'epoch': 15}
Test for Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3640887768447368, 'r': 0.3620809343253724, 'f1': 0.3630820797659034}, 'combined': 0.28143204268936056, 'stategy': 1, 'epoch': 15}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 15}
******************************
Epoch: 19
command: python train.py --model_name coref --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --accumulate_step 4 --max_epoch 20 --event_hidden_num 500 --p1_data_weight 0.2 --learning_rate 9e-4
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2023-01-24 08:47:38.598791: step: 2068/530, loss: 0.0021646814420819283
2023-01-24 08:47:39.633476: step: 2072/530, loss: 0.0010730999056249857
2023-01-24 08:47:40.671221: step: 2076/530, loss: 0.0001871632266556844
2023-01-24 08:47:41.704031: step: 2080/530, loss: 0.0014424140099436045
2023-01-24 08:47:42.742968: step: 2084/530, loss: 0.0009144091163761914
2023-01-24 08:47:43.792188: step: 2088/530, loss: 0.002994795562699437
2023-01-24 08:47:44.837879: step: 2092/530, loss: 0.00024580780882388353
2023-01-24 08:47:45.873399: step: 2096/530, loss: 0.00043128005927428603
2023-01-24 08:47:46.903061: step: 2100/530, loss: 0.00038046660483814776
2023-01-24 08:47:47.947487: step: 2104/530, loss: 0.006865813862532377
2023-01-24 08:47:48.987663: step: 2108/530, loss: 3.880581971316133e-06
2023-01-24 08:47:50.026081: step: 2112/530, loss: 0.0022267343010753393
2023-01-24 08:47:51.053566: step: 2116/530, loss: 0.02534710057079792
2023-01-24 08:47:52.078869: step: 2120/530, loss: 0.0003853960952255875
==================================================
Loss: 0.003
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3691397550111359, 'r': 0.3145042694497154, 'f1': 0.3396388319672131}, 'combined': 0.2502601919758412, 'stategy': 1, 'epoch': 19}
Test Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37510459577985067, 'r': 0.3916533279466088, 'f1': 0.38320037842258126}, 'combined': 0.2970261306433405, 'stategy': 1, 'epoch': 19}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3892996341075406, 'r': 0.3316803334236921, 'f1': 0.3581875731850117}, 'combined': 0.26392768550474544, 'stategy': 1, 'epoch': 19}
Test Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3732746852014944, 'r': 0.37053001839854227, 'f1': 0.37189728783912734}, 'combined': 0.2882648833968356, 'stategy': 1, 'epoch': 19}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.38297703353635176, 'r': 0.33283393047371745, 'f1': 0.35614920073025197}, 'combined': 0.26242572685386983, 'stategy': 1, 'epoch': 19}
Test Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.36358499223691937, 'r': 0.36391916961949006, 'f1': 0.3637520041763943}, 'combined': 0.2819513142419899, 'stategy': 1, 'epoch': 19}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 19}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.475, 'r': 0.41304347826086957, 'f1': 0.44186046511627913}, 'combined': 0.22093023255813957, 'stategy': 1, 'epoch': 19}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 19}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.37786862527716186, 'r': 0.32337523719165084, 'f1': 0.3485046012269939}, 'combined': 0.2567928640619955, 'stategy': 1, 'epoch': 12}
Test for Chinese: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.37377987703702326, 'r': 0.38889597500543227, 'f1': 0.3811881268521715}, 'combined': 0.2954663949763244, 'stategy': 1, 'epoch': 12}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4230769230769231, 'r': 0.3142857142857143, 'f1': 0.360655737704918}, 'combined': 0.24043715846994532, 'stategy': 1, 'epoch': 12}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3861756951102589, 'r': 0.32755319869883437, 'f1': 0.3544569521853916}, 'combined': 0.2611788068734464, 'stategy': 1, 'epoch': 2}
Test for Korean: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3702915838099576, 'r': 0.3696109007514834, 'f1': 0.36995092917903766}, 'combined': 0.28675622261724454, 'stategy': 1, 'epoch': 2}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.5125, 'r': 0.44565217391304346, 'f1': 0.47674418604651164}, 'combined': 0.23837209302325582, 'stategy': 1, 'epoch': 2}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3851604396498889, 'r': 0.33473146368054857, 'f1': 0.3581796575830439}, 'combined': 0.26392185295592707, 'stategy': 1, 'epoch': 15}
Test for Russian: {'template': {'p': 0.9418604651162791, 'r': 0.6585365853658537, 'f1': 0.7751196172248804}, 'slot': {'p': 0.3640887768447368, 'r': 0.3620809343253724, 'f1': 0.3630820797659034}, 'combined': 0.28143204268936056, 'stategy': 1, 'epoch': 15}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.6486842105263158, 'r': 0.22368421052631576, 'f1': 0.3326585695006748}, 'combined': 0.22177237966711652, 'stategy': 1, 'epoch': 15}