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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2023-01-22 17:10:02.399681: step: 2120/530, loss: 0.010328611359000206
==================================================
Loss: 0.022
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.330457410236822, 'r': 0.35177724315532666, 'f1': 0.34078420430672274}, 'combined': 0.2511041505417957, 'stategy': 1, 'epoch': 0}
Test Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3164748486044226, 'r': 0.28704988740875914, 'f1': 0.30104505826789996}, 'combined': 0.18696482566111683, 'stategy': 1, 'epoch': 0}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3385985644584724, 'r': 0.339883568498163, 'f1': 0.3392398496184316}, 'combined': 0.24996620498200223, 'stategy': 1, 'epoch': 0}
Test Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3012709339693999, 'r': 0.28488131837264724, 'f1': 0.29284698817971155}, 'combined': 0.18187339265897876, 'stategy': 1, 'epoch': 0}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3350544445433109, 'r': 0.32233890585096514, 'f1': 0.32857370093512306}, 'combined': 0.2421069375311433, 'stategy': 1, 'epoch': 0}
Test Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3252510122348801, 'r': 0.289932541014903, 'f1': 0.3065779374197252}, 'combined': 0.1904010348185662, 'stategy': 1, 'epoch': 0}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 0}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3357142857142857, 'r': 0.5108695652173914, 'f1': 0.4051724137931034}, 'combined': 0.2025862068965517, 'stategy': 1, 'epoch': 0}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, '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': 0.330457410236822, 'r': 0.35177724315532666, 'f1': 0.34078420430672274}, 'combined': 0.2511041505417957, 'stategy': 1, 'epoch': 0}
Test for Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3164748486044226, 'r': 0.28704988740875914, 'f1': 0.30104505826789996}, 'combined': 0.18696482566111683, 'stategy': 1, 'epoch': 0}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 0}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3385985644584724, 'r': 0.339883568498163, 'f1': 0.3392398496184316}, 'combined': 0.24996620498200223, 'stategy': 1, 'epoch': 0}
Test for Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3012709339693999, 'r': 0.28488131837264724, 'f1': 0.29284698817971155}, 'combined': 0.18187339265897876, 'stategy': 1, 'epoch': 0}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3357142857142857, 'r': 0.5108695652173914, 'f1': 0.4051724137931034}, 'combined': 0.2025862068965517, 'stategy': 1, 'epoch': 0}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3350544445433109, 'r': 0.32233890585096514, 'f1': 0.32857370093512306}, 'combined': 0.2421069375311433, 'stategy': 1, 'epoch': 0}
Test for Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3252510122348801, 'r': 0.289932541014903, 'f1': 0.3065779374197252}, 'combined': 0.1904010348185662, 'stategy': 1, 'epoch': 0}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, '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
2023-01-22 17:13:01.737365: step: 4/530, loss: 0.005641673691570759
2023-01-22 17:13:02.822053: step: 8/530, loss: 0.02767476812005043
2023-01-22 17:13:03.896255: step: 12/530, loss: 0.006464339327067137
2023-01-22 17:13:05.004839: step: 16/530, loss: 0.006023034919053316
2023-01-22 17:13:06.075019: step: 20/530, loss: 0.002565554343163967
2023-01-22 17:13:07.149697: step: 24/530, loss: 0.007445499766618013
2023-01-22 17:13:08.219606: step: 28/530, loss: 0.03772822394967079
2023-01-22 17:13:09.304393: step: 32/530, loss: 0.012982611544430256
2023-01-22 17:13:10.397442: step: 36/530, loss: 0.009248163551092148
2023-01-22 17:13:11.490494: step: 40/530, loss: 0.013611535541713238
2023-01-22 17:13:12.564589: step: 44/530, loss: 0.0074456194415688515
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2023-01-22 17:22:40.000798: step: 2120/530, loss: 0.012756621465086937
==================================================
Loss: 0.016
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34074334029772785, 'r': 0.3536747763621578, 'f1': 0.34708865389731314}, 'combined': 0.2557495344506518, 'stategy': 1, 'epoch': 1}
Test Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33347831328300875, 'r': 0.2823515689122211, 'f1': 0.30579264720970056}, 'combined': 0.1899133282670772, 'stategy': 1, 'epoch': 1}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.35259574920970843, 'r': 0.339883568498163, 'f1': 0.34612297700199396}, 'combined': 0.2550379830541008, 'stategy': 1, 'epoch': 1}
Test Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.31976221915024255, 'r': 0.2833896620757837, 'f1': 0.30047923267815135}, 'combined': 0.18661341818958874, 'stategy': 1, 'epoch': 1}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3422990007715465, 'r': 0.32281328915267293, 'f1': 0.3322707097333176}, 'combined': 0.24483104927718138, 'stategy': 1, 'epoch': 1}
Test Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.34442683573720273, 'r': 0.2894000631638035, 'f1': 0.31452482748992144}, 'combined': 0.19533647180953018, 'stategy': 1, 'epoch': 1}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 1}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3870967741935484, 'r': 0.5217391304347826, 'f1': 0.4444444444444444}, 'combined': 0.2222222222222222, 'stategy': 1, 'epoch': 1}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 1}
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': 0.34074334029772785, 'r': 0.3536747763621578, 'f1': 0.34708865389731314}, 'combined': 0.2557495344506518, 'stategy': 1, 'epoch': 1}
Test for Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33347831328300875, 'r': 0.2823515689122211, 'f1': 0.30579264720970056}, 'combined': 0.1899133282670772, 'stategy': 1, 'epoch': 1}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 1}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.35259574920970843, 'r': 0.339883568498163, 'f1': 0.34612297700199396}, 'combined': 0.2550379830541008, 'stategy': 1, 'epoch': 1}
Test for Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.31976221915024255, 'r': 0.2833896620757837, 'f1': 0.30047923267815135}, 'combined': 0.18661341818958874, 'stategy': 1, 'epoch': 1}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3870967741935484, 'r': 0.5217391304347826, 'f1': 0.4444444444444444}, 'combined': 0.2222222222222222, 'stategy': 1, 'epoch': 1}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3422990007715465, 'r': 0.32281328915267293, 'f1': 0.3322707097333176}, 'combined': 0.24483104927718138, 'stategy': 1, 'epoch': 1}
Test for Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.34442683573720273, 'r': 0.2894000631638035, 'f1': 0.31452482748992144}, 'combined': 0.19533647180953018, 'stategy': 1, 'epoch': 1}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, '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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2023-01-22 17:35:02.976673: step: 2120/530, loss: 0.089204341173172
==================================================
Loss: 0.013
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.33298328673106253, 'r': 0.3550979262672811, 'f1': 0.34368522891250164}, 'combined': 0.25324174761973806, 'stategy': 1, 'epoch': 2}
Test Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3335734214933513, 'r': 0.28507164804179574, 'f1': 0.3074212652482725}, 'combined': 0.19092478578576927, 'stategy': 1, 'epoch': 2}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3442776211104259, 'r': 0.3403579517998708, 'f1': 0.3423065660277327}, 'combined': 0.2522258907572767, 'stategy': 1, 'epoch': 2}
Test Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.32101106394999596, 'r': 0.2873541175813515, 'f1': 0.3032515792011966}, 'combined': 0.18833519129337475, 'stategy': 1, 'epoch': 2}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.33685551847137524, 'r': 0.32471082235950405, 'f1': 0.3306716973593404}, 'combined': 0.24365282963319815, 'stategy': 1, 'epoch': 2}
Test Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.34500193493964737, 'r': 0.2928940415084864, 'f1': 0.3168197160150526}, 'combined': 0.1967617183672432, 'stategy': 1, 'epoch': 2}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3138888888888889, 'r': 0.4035714285714286, 'f1': 0.35312499999999997}, 'combined': 0.23541666666666664, 'stategy': 1, 'epoch': 2}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.36363636363636365, 'r': 0.5217391304347826, 'f1': 0.42857142857142855}, 'combined': 0.21428571428571427, 'stategy': 1, 'epoch': 2}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 2}
==================================================
Current best result:
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Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34074334029772785, 'r': 0.3536747763621578, 'f1': 0.34708865389731314}, 'combined': 0.2557495344506518, 'stategy': 1, 'epoch': 1}
Test for Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33347831328300875, 'r': 0.2823515689122211, 'f1': 0.30579264720970056}, 'combined': 0.1899133282670772, 'stategy': 1, 'epoch': 1}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 1}
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Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.35259574920970843, 'r': 0.339883568498163, 'f1': 0.34612297700199396}, 'combined': 0.2550379830541008, 'stategy': 1, 'epoch': 1}
Test for Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.31976221915024255, 'r': 0.2833896620757837, 'f1': 0.30047923267815135}, 'combined': 0.18661341818958874, 'stategy': 1, 'epoch': 1}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3870967741935484, 'r': 0.5217391304347826, 'f1': 0.4444444444444444}, 'combined': 0.2222222222222222, 'stategy': 1, 'epoch': 1}
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Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3422990007715465, 'r': 0.32281328915267293, 'f1': 0.3322707097333176}, 'combined': 0.24483104927718138, 'stategy': 1, 'epoch': 1}
Test for Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.34442683573720273, 'r': 0.2894000631638035, 'f1': 0.31452482748992144}, 'combined': 0.19533647180953018, 'stategy': 1, 'epoch': 1}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 1}
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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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==================================================
Loss: 0.011
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34038473737630043, 'r': 0.36363682569802114, 'f1': 0.35162680209698555}, 'combined': 0.2590934331240946, 'stategy': 1, 'epoch': 3}
Test Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33394714661777014, 'r': 0.29100636614268593, 'f1': 0.31100151815037574}, 'combined': 0.19314831127233864, 'stategy': 1, 'epoch': 3}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34946305457735094, 'r': 0.3474737013254875, 'f1': 0.3484655387222301}, 'combined': 0.25676408116374844, 'stategy': 1, 'epoch': 3}
Test Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3180010973724229, 'r': 0.2903214766317966, 'f1': 0.3035315541620955}, 'combined': 0.1885090704796172, 'stategy': 1, 'epoch': 3}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34039493836931506, 'r': 0.331352188583413, 'f1': 0.33581269881434356}, 'combined': 0.24744093596846367, 'stategy': 1, 'epoch': 3}
Test Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3460110194391069, 'r': 0.2995574176190491, 'f1': 0.3211128720085679}, 'combined': 0.19942799419479482, 'stategy': 1, 'epoch': 3}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 3}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3870967741935484, 'r': 0.5217391304347826, 'f1': 0.4444444444444444}, 'combined': 0.2222222222222222, 'stategy': 1, 'epoch': 3}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 3}
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': 0.34038473737630043, 'r': 0.36363682569802114, 'f1': 0.35162680209698555}, 'combined': 0.2590934331240946, 'stategy': 1, 'epoch': 3}
Test for Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33394714661777014, 'r': 0.29100636614268593, 'f1': 0.31100151815037574}, 'combined': 0.19314831127233864, 'stategy': 1, 'epoch': 3}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 3}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34946305457735094, 'r': 0.3474737013254875, 'f1': 0.3484655387222301}, 'combined': 0.25676408116374844, 'stategy': 1, 'epoch': 3}
Test for Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3180010973724229, 'r': 0.2903214766317966, 'f1': 0.3035315541620955}, 'combined': 0.1885090704796172, 'stategy': 1, 'epoch': 3}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3870967741935484, 'r': 0.5217391304347826, 'f1': 0.4444444444444444}, 'combined': 0.2222222222222222, 'stategy': 1, 'epoch': 3}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34039493836931506, 'r': 0.331352188583413, 'f1': 0.33581269881434356}, 'combined': 0.24744093596846367, 'stategy': 1, 'epoch': 3}
Test for Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3460110194391069, 'r': 0.2995574176190491, 'f1': 0.3211128720085679}, 'combined': 0.19942799419479482, 'stategy': 1, 'epoch': 3}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 3}
******************************
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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2023-01-22 17:59:42.616994: step: 2120/530, loss: 0.02791227400302887
==================================================
Loss: 0.012
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.33718307593307595, 'r': 0.3550979262672811, 'f1': 0.3459087008185899}, 'combined': 0.2548800953400136, 'stategy': 1, 'epoch': 4}
Test Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33073405461480215, 'r': 0.2885335669339817, 'f1': 0.30819591777100425}, 'combined': 0.19140588577357107, 'stategy': 1, 'epoch': 4}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3504253708142644, 'r': 0.3417811017049941, 'f1': 0.3460492614765262}, 'combined': 0.25498366635112457, 'stategy': 1, 'epoch': 4}
Test Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3179244390742019, 'r': 0.28710683766048106, 'f1': 0.3017307826140814}, 'combined': 0.18739069657085058, 'stategy': 1, 'epoch': 4}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.33917510550090635, 'r': 0.3256595889629196, 'f1': 0.3322799678285743}, 'combined': 0.24483787103158103, 'stategy': 1, 'epoch': 4}
Test Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3437305177287306, 'r': 0.29758308395666017, 'f1': 0.31899646989216585}, 'combined': 0.19811359709092408, 'stategy': 1, 'epoch': 4}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 4}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3984375, 'r': 0.5543478260869565, 'f1': 0.4636363636363637}, 'combined': 0.23181818181818184, 'stategy': 1, 'epoch': 4}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 4}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34038473737630043, 'r': 0.36363682569802114, 'f1': 0.35162680209698555}, 'combined': 0.2590934331240946, 'stategy': 1, 'epoch': 3}
Test for Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33394714661777014, 'r': 0.29100636614268593, 'f1': 0.31100151815037574}, 'combined': 0.19314831127233864, 'stategy': 1, 'epoch': 3}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 3}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3504253708142644, 'r': 0.3417811017049941, 'f1': 0.3460492614765262}, 'combined': 0.25498366635112457, 'stategy': 1, 'epoch': 4}
Test for Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3179244390742019, 'r': 0.28710683766048106, 'f1': 0.3017307826140814}, 'combined': 0.18739069657085058, 'stategy': 1, 'epoch': 4}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3984375, 'r': 0.5543478260869565, 'f1': 0.4636363636363637}, 'combined': 0.23181818181818184, 'stategy': 1, 'epoch': 4}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34039493836931506, 'r': 0.331352188583413, 'f1': 0.33581269881434356}, 'combined': 0.24744093596846367, 'stategy': 1, 'epoch': 3}
Test for Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3460110194391069, 'r': 0.2995574176190491, 'f1': 0.3211128720085679}, 'combined': 0.19942799419479482, 'stategy': 1, 'epoch': 3}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 3}
******************************
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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2023-01-22 18:12:02.359517: step: 2120/530, loss: 0.007340516895055771
==================================================
Loss: 0.012
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.33898487773487773, 'r': 0.3569954594741122, 'f1': 0.34775712965407973}, 'combined': 0.25624209553458505, 'stategy': 1, 'epoch': 5}
Test Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.334818683740225, 'r': 0.2877917271713704, 'f1': 0.30952918741516544}, 'combined': 0.1922339163946817, 'stategy': 1, 'epoch': 5}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34704940385073585, 'r': 0.3417811017049941, 'f1': 0.3443951063069443}, 'combined': 0.2537648151735379, 'stategy': 1, 'epoch': 5}
Test Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.31923840478968935, 'r': 0.2873461408097105, 'f1': 0.3024538764795599}, 'combined': 0.1878397759188846, 'stategy': 1, 'epoch': 5}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3471622333667169, 'r': 0.331352188583413, 'f1': 0.3390730162785604}, 'combined': 0.24984327515262345, 'stategy': 1, 'epoch': 5}
Test Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.344671684876126, 'r': 0.2936344166318823, 'f1': 0.31711264823890906}, 'combined': 0.19694364469574352, 'stategy': 1, 'epoch': 5}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 5}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.375, 'r': 0.5217391304347826, 'f1': 0.43636363636363634}, 'combined': 0.21818181818181817, 'stategy': 1, 'epoch': 5}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 5}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34038473737630043, 'r': 0.36363682569802114, 'f1': 0.35162680209698555}, 'combined': 0.2590934331240946, 'stategy': 1, 'epoch': 3}
Test for Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33394714661777014, 'r': 0.29100636614268593, 'f1': 0.31100151815037574}, 'combined': 0.19314831127233864, 'stategy': 1, 'epoch': 3}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 3}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3504253708142644, 'r': 0.3417811017049941, 'f1': 0.3460492614765262}, 'combined': 0.25498366635112457, 'stategy': 1, 'epoch': 4}
Test for Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3179244390742019, 'r': 0.28710683766048106, 'f1': 0.3017307826140814}, 'combined': 0.18739069657085058, 'stategy': 1, 'epoch': 4}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3984375, 'r': 0.5543478260869565, 'f1': 0.4636363636363637}, 'combined': 0.23181818181818184, 'stategy': 1, 'epoch': 4}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3471622333667169, 'r': 0.331352188583413, 'f1': 0.3390730162785604}, 'combined': 0.24984327515262345, 'stategy': 1, 'epoch': 5}
Test for Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.344671684876126, 'r': 0.2936344166318823, 'f1': 0.31711264823890906}, 'combined': 0.19694364469574352, 'stategy': 1, 'epoch': 5}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 5}
******************************
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
2023-01-22 18:14:43.457192: step: 4/530, loss: 0.013390426523983479
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2023-01-22 18:14:50.023342: step: 28/530, loss: 0.06323400139808655
2023-01-22 18:14:51.139123: step: 32/530, loss: 0.019175894558429718
2023-01-22 18:14:52.229445: step: 36/530, loss: 0.0103736761957407
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2023-01-22 18:24:22.144219: step: 2120/530, loss: 0.008159960620105267
==================================================
Loss: 0.010
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34010072445348244, 'r': 0.3626880590946056, 'f1': 0.3510314180768726}, 'combined': 0.25865472910927456, 'stategy': 1, 'epoch': 6}
Test Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3365448057195115, 'r': 0.28927540669659296, 'f1': 0.31112493209601644}, 'combined': 0.19322495782805232, 'stategy': 1, 'epoch': 6}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3501312439742484, 'r': 0.3474737013254875, 'f1': 0.34879741066387027}, 'combined': 0.2570086183839044, 'stategy': 1, 'epoch': 6}
Test Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.32076756427160497, 'r': 0.28808798057232177, 'f1': 0.30355075389121133}, 'combined': 0.1885209945219102, 'stategy': 1, 'epoch': 6}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3447809032815958, 'r': 0.33562163829878294, 'f1': 0.34013962189126656}, 'combined': 0.25062919507777537, 'stategy': 1, 'epoch': 6}
Test Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.34636450813867986, 'r': 0.29610233370986844, 'f1': 0.3192673379969098}, 'combined': 0.1982818204401861, 'stategy': 1, 'epoch': 6}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 6}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3870967741935484, 'r': 0.5217391304347826, 'f1': 0.4444444444444444}, 'combined': 0.2222222222222222, 'stategy': 1, 'epoch': 6}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 6}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34038473737630043, 'r': 0.36363682569802114, 'f1': 0.35162680209698555}, 'combined': 0.2590934331240946, 'stategy': 1, 'epoch': 3}
Test for Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33394714661777014, 'r': 0.29100636614268593, 'f1': 0.31100151815037574}, 'combined': 0.19314831127233864, 'stategy': 1, 'epoch': 3}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 3}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3504253708142644, 'r': 0.3417811017049941, 'f1': 0.3460492614765262}, 'combined': 0.25498366635112457, 'stategy': 1, 'epoch': 4}
Test for Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3179244390742019, 'r': 0.28710683766048106, 'f1': 0.3017307826140814}, 'combined': 0.18739069657085058, 'stategy': 1, 'epoch': 4}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3984375, 'r': 0.5543478260869565, 'f1': 0.4636363636363637}, 'combined': 0.23181818181818184, 'stategy': 1, 'epoch': 4}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3447809032815958, 'r': 0.33562163829878294, 'f1': 0.34013962189126656}, 'combined': 0.25062919507777537, 'stategy': 1, 'epoch': 6}
Test for Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.34636450813867986, 'r': 0.29610233370986844, 'f1': 0.3192673379969098}, 'combined': 0.1982818204401861, 'stategy': 1, 'epoch': 6}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 6}
******************************
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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2023-01-22 18:36:46.768171: step: 2120/530, loss: 0.0016149027505889535
==================================================
Loss: 0.008
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.33933928571428573, 'r': 0.35414915966386556, 'f1': 0.34658608568775695}, 'combined': 0.2553792210330841, 'stategy': 1, 'epoch': 7}
Test Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33212666223409204, 'r': 0.2838352484374436, 'f1': 0.30608793191493916}, 'combined': 0.19009671561033065, 'stategy': 1, 'epoch': 7}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34964647290162165, 'r': 0.3403579517998708, 'f1': 0.34493969345871517}, 'combined': 0.254166089916948, 'stategy': 1, 'epoch': 7}
Test Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.31974305221457255, 'r': 0.28685158096796964, 'f1': 0.30240557701628495}, 'combined': 0.18780977941011381, 'stategy': 1, 'epoch': 7}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34305908858973777, 'r': 0.32613397226462737, 'f1': 0.3343824968549779}, 'combined': 0.24638710294577318, 'stategy': 1, 'epoch': 7}
Test Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.34803433338900086, 'r': 0.2968427088332643, 'f1': 0.3204066745318026}, 'combined': 0.19898940839343532, 'stategy': 1, 'epoch': 7}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 7}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.375, 'r': 0.5217391304347826, 'f1': 0.43636363636363634}, 'combined': 0.21818181818181817, 'stategy': 1, 'epoch': 7}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 7}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34038473737630043, 'r': 0.36363682569802114, 'f1': 0.35162680209698555}, 'combined': 0.2590934331240946, 'stategy': 1, 'epoch': 3}
Test for Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33394714661777014, 'r': 0.29100636614268593, 'f1': 0.31100151815037574}, 'combined': 0.19314831127233864, 'stategy': 1, 'epoch': 3}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 3}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3504253708142644, 'r': 0.3417811017049941, 'f1': 0.3460492614765262}, 'combined': 0.25498366635112457, 'stategy': 1, 'epoch': 4}
Test for Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3179244390742019, 'r': 0.28710683766048106, 'f1': 0.3017307826140814}, 'combined': 0.18739069657085058, 'stategy': 1, 'epoch': 4}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3984375, 'r': 0.5543478260869565, 'f1': 0.4636363636363637}, 'combined': 0.23181818181818184, 'stategy': 1, 'epoch': 4}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3447809032815958, 'r': 0.33562163829878294, 'f1': 0.34013962189126656}, 'combined': 0.25062919507777537, 'stategy': 1, 'epoch': 6}
Test for Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.34636450813867986, 'r': 0.29610233370986844, 'f1': 0.3192673379969098}, 'combined': 0.1982818204401861, 'stategy': 1, 'epoch': 6}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 6}
******************************
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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==================================================
Loss: 0.008
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3407029220779221, 'r': 0.3555723095689889, 'f1': 0.3479788433479241}, 'combined': 0.25640546351952304, 'stategy': 1, 'epoch': 8}
Test Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3306613475177803, 'r': 0.2806206094661281, 'f1': 0.3035927620869508}, 'combined': 0.18854708382242208, 'stategy': 1, 'epoch': 8}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34964647290162165, 'r': 0.3403579517998708, 'f1': 0.34493969345871517}, 'combined': 0.254166089916948, 'stategy': 1, 'epoch': 8}
Test Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.32111883150957476, 'r': 0.28586246128448795, 'f1': 0.30246671727746444}, 'combined': 0.18784775073021476, 'stategy': 1, 'epoch': 8}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34106308060570584, 'r': 0.32423643905779626, 'f1': 0.3324369715631491}, 'combined': 0.24495355799389934, 'stategy': 1, 'epoch': 8}
Test Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3473118877755518, 'r': 0.29314083321628503, 'f1': 0.317935400479761}, 'combined': 0.1974546171400621, 'stategy': 1, 'epoch': 8}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 8}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3870967741935484, 'r': 0.5217391304347826, 'f1': 0.4444444444444444}, 'combined': 0.2222222222222222, 'stategy': 1, 'epoch': 8}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5208333333333334, 'r': 0.3232758620689655, 'f1': 0.39893617021276595}, 'combined': 0.26595744680851063, 'stategy': 1, 'epoch': 8}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34038473737630043, 'r': 0.36363682569802114, 'f1': 0.35162680209698555}, 'combined': 0.2590934331240946, 'stategy': 1, 'epoch': 3}
Test for Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33394714661777014, 'r': 0.29100636614268593, 'f1': 0.31100151815037574}, 'combined': 0.19314831127233864, 'stategy': 1, 'epoch': 3}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 3}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3504253708142644, 'r': 0.3417811017049941, 'f1': 0.3460492614765262}, 'combined': 0.25498366635112457, 'stategy': 1, 'epoch': 4}
Test for Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3179244390742019, 'r': 0.28710683766048106, 'f1': 0.3017307826140814}, 'combined': 0.18739069657085058, 'stategy': 1, 'epoch': 4}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3984375, 'r': 0.5543478260869565, 'f1': 0.4636363636363637}, 'combined': 0.23181818181818184, 'stategy': 1, 'epoch': 4}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3447809032815958, 'r': 0.33562163829878294, 'f1': 0.34013962189126656}, 'combined': 0.25062919507777537, 'stategy': 1, 'epoch': 6}
Test for Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.34636450813867986, 'r': 0.29610233370986844, 'f1': 0.3192673379969098}, 'combined': 0.1982818204401861, 'stategy': 1, 'epoch': 6}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 6}
******************************
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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2023-01-22 19:01:13.616189: step: 2120/530, loss: 0.001871609129011631
==================================================
Loss: 0.006
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.33460531297324586, 'r': 0.3593673759826511, 'f1': 0.3465445693373415}, 'combined': 0.2553486300380411, 'stategy': 1, 'epoch': 9}
Test Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33482382929590704, 'r': 0.2868026074878887, 'f1': 0.30895837631353806}, 'combined': 0.19187941265788155, 'stategy': 1, 'epoch': 9}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3463071521960457, 'r': 0.3436786349118252, 'f1': 0.3449878868543464}, 'combined': 0.2542016008400447, 'stategy': 1, 'epoch': 9}
Test Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.32384618181262975, 'r': 0.2895716600975443, 'f1': 0.30575138209777264}, 'combined': 0.18988770046072198, 'stategy': 1, 'epoch': 9}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.33781331386195423, 'r': 0.3275571221697507, 'f1': 0.3326061722224636}, 'combined': 0.24507823216392052, 'stategy': 1, 'epoch': 9}
Test Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.34849030703267064, 'r': 0.2958555420020698, 'f1': 0.3200231329931626}, 'combined': 0.1987512089115431, 'stategy': 1, 'epoch': 9}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3138888888888889, 'r': 0.4035714285714286, 'f1': 0.35312499999999997}, 'combined': 0.23541666666666664, 'stategy': 1, 'epoch': 9}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.35294117647058826, 'r': 0.5217391304347826, 'f1': 0.4210526315789474}, 'combined': 0.2105263157894737, 'stategy': 1, 'epoch': 9}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 9}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34038473737630043, 'r': 0.36363682569802114, 'f1': 0.35162680209698555}, 'combined': 0.2590934331240946, 'stategy': 1, 'epoch': 3}
Test for Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33394714661777014, 'r': 0.29100636614268593, 'f1': 0.31100151815037574}, 'combined': 0.19314831127233864, 'stategy': 1, 'epoch': 3}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 3}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3504253708142644, 'r': 0.3417811017049941, 'f1': 0.3460492614765262}, 'combined': 0.25498366635112457, 'stategy': 1, 'epoch': 4}
Test for Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3179244390742019, 'r': 0.28710683766048106, 'f1': 0.3017307826140814}, 'combined': 0.18739069657085058, 'stategy': 1, 'epoch': 4}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3984375, 'r': 0.5543478260869565, 'f1': 0.4636363636363637}, 'combined': 0.23181818181818184, 'stategy': 1, 'epoch': 4}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3447809032815958, 'r': 0.33562163829878294, 'f1': 0.34013962189126656}, 'combined': 0.25062919507777537, 'stategy': 1, 'epoch': 6}
Test for Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.34636450813867986, 'r': 0.29610233370986844, 'f1': 0.3192673379969098}, 'combined': 0.1982818204401861, 'stategy': 1, 'epoch': 6}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 6}
******************************
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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==================================================
Loss: 0.005
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3383481344237158, 'r': 0.3588929926809433, 'f1': 0.34831787687450666}, 'combined': 0.2566552776970049, 'stategy': 1, 'epoch': 10}
Test Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.333764327385087, 'r': 0.2865553275670183, 'f1': 0.3083634232786115}, 'combined': 0.1915099155098745, 'stategy': 1, 'epoch': 10}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3443320163470188, 'r': 0.3436786349118252, 'f1': 0.344005015381827}, 'combined': 0.2534773797550304, 'stategy': 1, 'epoch': 10}
Test Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.31718826911741993, 'r': 0.28487334160100625, 'f1': 0.30016357306786584}, 'combined': 0.18641737695793775, 'stategy': 1, 'epoch': 10}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3459025707760525, 'r': 0.3327753384885363, 'f1': 0.3392119988074635}, 'combined': 0.2499456833318152, 'stategy': 1, 'epoch': 10}
Test Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3443108757817616, 'r': 0.2926472498006878, 'f1': 0.31638384636936684}, 'combined': 0.1964910203767647, 'stategy': 1, 'epoch': 10}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 10}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3870967741935484, 'r': 0.5217391304347826, 'f1': 0.4444444444444444}, 'combined': 0.2222222222222222, 'stategy': 1, 'epoch': 10}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5208333333333334, 'r': 0.3232758620689655, 'f1': 0.39893617021276595}, 'combined': 0.26595744680851063, 'stategy': 1, 'epoch': 10}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34038473737630043, 'r': 0.36363682569802114, 'f1': 0.35162680209698555}, 'combined': 0.2590934331240946, 'stategy': 1, 'epoch': 3}
Test for Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33394714661777014, 'r': 0.29100636614268593, 'f1': 0.31100151815037574}, 'combined': 0.19314831127233864, 'stategy': 1, 'epoch': 3}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 3}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3504253708142644, 'r': 0.3417811017049941, 'f1': 0.3460492614765262}, 'combined': 0.25498366635112457, 'stategy': 1, 'epoch': 4}
Test for Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3179244390742019, 'r': 0.28710683766048106, 'f1': 0.3017307826140814}, 'combined': 0.18739069657085058, 'stategy': 1, 'epoch': 4}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3984375, 'r': 0.5543478260869565, 'f1': 0.4636363636363637}, 'combined': 0.23181818181818184, 'stategy': 1, 'epoch': 4}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3447809032815958, 'r': 0.33562163829878294, 'f1': 0.34013962189126656}, 'combined': 0.25062919507777537, 'stategy': 1, 'epoch': 6}
Test for Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.34636450813867986, 'r': 0.29610233370986844, 'f1': 0.3192673379969098}, 'combined': 0.1982818204401861, 'stategy': 1, 'epoch': 6}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 6}
******************************
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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2023-01-22 19:25:30.580467: step: 2120/530, loss: 0.004256460350006819
==================================================
Loss: 0.006
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3395630289817902, 'r': 0.3588929926809433, 'f1': 0.3489605297838692}, 'combined': 0.25712881141969307, 'stategy': 1, 'epoch': 11}
Test Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3375376289678138, 'r': 0.2877917271713704, 'f1': 0.3106859969783828}, 'combined': 0.19295235601815353, 'stategy': 1, 'epoch': 11}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34763654625437984, 'r': 0.3436786349118252, 'f1': 0.34564626068422116}, 'combined': 0.2546867183988998, 'stategy': 1, 'epoch': 11}
Test Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.32501668097194036, 'r': 0.2893323569483149, 'f1': 0.30613816104107416}, 'combined': 0.1901279105412987, 'stategy': 1, 'epoch': 11}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.33980827437688704, 'r': 0.3275571221697507, 'f1': 0.333570248083978}, 'combined': 0.2457886038513522, 'stategy': 1, 'epoch': 11}
Test Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3480855563857105, 'r': 0.2958555420020698, 'f1': 0.3198523629115227}, 'combined': 0.1986451517029457, 'stategy': 1, 'epoch': 11}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3138888888888889, 'r': 0.4035714285714286, 'f1': 0.35312499999999997}, 'combined': 0.23541666666666664, 'stategy': 1, 'epoch': 11}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3870967741935484, 'r': 0.5217391304347826, 'f1': 0.4444444444444444}, 'combined': 0.2222222222222222, 'stategy': 1, 'epoch': 11}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 11}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34038473737630043, 'r': 0.36363682569802114, 'f1': 0.35162680209698555}, 'combined': 0.2590934331240946, 'stategy': 1, 'epoch': 3}
Test for Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33394714661777014, 'r': 0.29100636614268593, 'f1': 0.31100151815037574}, 'combined': 0.19314831127233864, 'stategy': 1, 'epoch': 3}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 3}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3504253708142644, 'r': 0.3417811017049941, 'f1': 0.3460492614765262}, 'combined': 0.25498366635112457, 'stategy': 1, 'epoch': 4}
Test for Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3179244390742019, 'r': 0.28710683766048106, 'f1': 0.3017307826140814}, 'combined': 0.18739069657085058, 'stategy': 1, 'epoch': 4}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3984375, 'r': 0.5543478260869565, 'f1': 0.4636363636363637}, 'combined': 0.23181818181818184, 'stategy': 1, 'epoch': 4}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3447809032815958, 'r': 0.33562163829878294, 'f1': 0.34013962189126656}, 'combined': 0.25062919507777537, 'stategy': 1, 'epoch': 6}
Test for Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.34636450813867986, 'r': 0.29610233370986844, 'f1': 0.3192673379969098}, 'combined': 0.1982818204401861, 'stategy': 1, 'epoch': 6}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 6}
******************************
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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2023-01-22 19:37:43.572339: step: 2120/530, loss: 0.005693155340850353
==================================================
Loss: 0.005
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.33431780630401625, 'r': 0.35652107617240447, 'f1': 0.3450626393808212}, 'combined': 0.25425668164902615, 'stategy': 1, 'epoch': 12}
Test Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3320406844664242, 'r': 0.28606076772527744, 'f1': 0.3073405272797614}, 'combined': 0.1908746432579571, 'stategy': 1, 'epoch': 12}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34638200115102286, 'r': 0.3417811017049941, 'f1': 0.3440661711528785}, 'combined': 0.253522441902121, 'stategy': 1, 'epoch': 12}
Test Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.321830882164218, 'r': 0.28808798057232177, 'f1': 0.304026042127993}, 'combined': 0.188816173532122, 'stategy': 1, 'epoch': 12}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.33636339248712327, 'r': 0.32423643905779626, 'f1': 0.3301886055719007}, 'combined': 0.24329686726350577, 'stategy': 1, 'epoch': 12}
Test Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3465216520279545, 'r': 0.2948683751708754, 'f1': 0.3186151083179699}, 'combined': 0.1978767514816866, 'stategy': 1, 'epoch': 12}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3138888888888889, 'r': 0.4035714285714286, 'f1': 0.35312499999999997}, 'combined': 0.23541666666666664, 'stategy': 1, 'epoch': 12}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.35294117647058826, 'r': 0.5217391304347826, 'f1': 0.4210526315789474}, 'combined': 0.2105263157894737, 'stategy': 1, 'epoch': 12}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 12}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34038473737630043, 'r': 0.36363682569802114, 'f1': 0.35162680209698555}, 'combined': 0.2590934331240946, 'stategy': 1, 'epoch': 3}
Test for Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33394714661777014, 'r': 0.29100636614268593, 'f1': 0.31100151815037574}, 'combined': 0.19314831127233864, 'stategy': 1, 'epoch': 3}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 3}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3504253708142644, 'r': 0.3417811017049941, 'f1': 0.3460492614765262}, 'combined': 0.25498366635112457, 'stategy': 1, 'epoch': 4}
Test for Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3179244390742019, 'r': 0.28710683766048106, 'f1': 0.3017307826140814}, 'combined': 0.18739069657085058, 'stategy': 1, 'epoch': 4}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3984375, 'r': 0.5543478260869565, 'f1': 0.4636363636363637}, 'combined': 0.23181818181818184, 'stategy': 1, 'epoch': 4}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3447809032815958, 'r': 0.33562163829878294, 'f1': 0.34013962189126656}, 'combined': 0.25062919507777537, 'stategy': 1, 'epoch': 6}
Test for Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.34636450813867986, 'r': 0.29610233370986844, 'f1': 0.3192673379969098}, 'combined': 0.1982818204401861, 'stategy': 1, 'epoch': 6}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 6}
******************************
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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==================================================
Loss: 0.005
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3353593710211357, 'r': 0.3569954594741122, 'f1': 0.3458393513655462}, 'combined': 0.254828995743034, 'stategy': 1, 'epoch': 13}
Test Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3353026945447007, 'r': 0.2875444472505, 'f1': 0.30959258378088983}, 'combined': 0.1922732888744474, 'stategy': 1, 'epoch': 13}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3463071521960457, 'r': 0.3436786349118252, 'f1': 0.3449878868543464}, 'combined': 0.2542016008400447, 'stategy': 1, 'epoch': 13}
Test Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3215244045174571, 'r': 0.28685955773961064, 'f1': 0.3032044044691546}, 'combined': 0.188305893301896, 'stategy': 1, 'epoch': 13}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3404785076596817, 'r': 0.3275571221697507, 'f1': 0.3338928498712933}, 'combined': 0.24602631043147927, 'stategy': 1, 'epoch': 13}
Test Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3465677113481918, 'r': 0.2938812083396809, 'f1': 0.31805733338471875}, 'combined': 0.19753034389156218, 'stategy': 1, 'epoch': 13}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3138888888888889, 'r': 0.4035714285714286, 'f1': 0.35312499999999997}, 'combined': 0.23541666666666664, 'stategy': 1, 'epoch': 13}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.38636363636363635, 'r': 0.5543478260869565, 'f1': 0.4553571428571429}, 'combined': 0.22767857142857145, 'stategy': 1, 'epoch': 13}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 13}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34038473737630043, 'r': 0.36363682569802114, 'f1': 0.35162680209698555}, 'combined': 0.2590934331240946, 'stategy': 1, 'epoch': 3}
Test for Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33394714661777014, 'r': 0.29100636614268593, 'f1': 0.31100151815037574}, 'combined': 0.19314831127233864, 'stategy': 1, 'epoch': 3}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 3}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3504253708142644, 'r': 0.3417811017049941, 'f1': 0.3460492614765262}, 'combined': 0.25498366635112457, 'stategy': 1, 'epoch': 4}
Test for Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3179244390742019, 'r': 0.28710683766048106, 'f1': 0.3017307826140814}, 'combined': 0.18739069657085058, 'stategy': 1, 'epoch': 4}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3984375, 'r': 0.5543478260869565, 'f1': 0.4636363636363637}, 'combined': 0.23181818181818184, 'stategy': 1, 'epoch': 4}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3447809032815958, 'r': 0.33562163829878294, 'f1': 0.34013962189126656}, 'combined': 0.25062919507777537, 'stategy': 1, 'epoch': 6}
Test for Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.34636450813867986, 'r': 0.29610233370986844, 'f1': 0.3192673379969098}, 'combined': 0.1982818204401861, 'stategy': 1, 'epoch': 6}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 6}
******************************
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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2023-01-22 20:02:04.017980: step: 2120/530, loss: 0.002504725707694888
==================================================
Loss: 0.004
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.33669626941685765, 'r': 0.3584186093792356, 'f1': 0.3472180278361345}, 'combined': 0.25584486261609907, 'stategy': 1, 'epoch': 14}
Test Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3370200415750063, 'r': 0.29001724645920424, 'f1': 0.3117569762575816}, 'combined': 0.19361749051786648, 'stategy': 1, 'epoch': 14}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3481342977158401, 'r': 0.3474737013254875, 'f1': 0.34780368584716415}, 'combined': 0.2562764000979104, 'stategy': 1, 'epoch': 14}
Test Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3188879784397555, 'r': 0.2873461408097105, 'f1': 0.3022965123398723}, 'combined': 0.18774204450581544, 'stategy': 1, 'epoch': 14}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3418569266734086, 'r': 0.3327753384885363, 'f1': 0.3372550065066512}, 'combined': 0.24850368900490086, 'stategy': 1, 'epoch': 14}
Test Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.346319521344185, 'r': 0.2970895005410629, 'f1': 0.3198211095091357}, 'combined': 0.19862574169514746, 'stategy': 1, 'epoch': 14}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3138888888888889, 'r': 0.4035714285714286, 'f1': 0.35312499999999997}, 'combined': 0.23541666666666664, 'stategy': 1, 'epoch': 14}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3870967741935484, 'r': 0.5217391304347826, 'f1': 0.4444444444444444}, 'combined': 0.2222222222222222, 'stategy': 1, 'epoch': 14}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 14}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34038473737630043, 'r': 0.36363682569802114, 'f1': 0.35162680209698555}, 'combined': 0.2590934331240946, 'stategy': 1, 'epoch': 3}
Test for Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33394714661777014, 'r': 0.29100636614268593, 'f1': 0.31100151815037574}, 'combined': 0.19314831127233864, 'stategy': 1, 'epoch': 3}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 3}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3504253708142644, 'r': 0.3417811017049941, 'f1': 0.3460492614765262}, 'combined': 0.25498366635112457, 'stategy': 1, 'epoch': 4}
Test for Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3179244390742019, 'r': 0.28710683766048106, 'f1': 0.3017307826140814}, 'combined': 0.18739069657085058, 'stategy': 1, 'epoch': 4}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3984375, 'r': 0.5543478260869565, 'f1': 0.4636363636363637}, 'combined': 0.23181818181818184, 'stategy': 1, 'epoch': 4}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3447809032815958, 'r': 0.33562163829878294, 'f1': 0.34013962189126656}, 'combined': 0.25062919507777537, 'stategy': 1, 'epoch': 6}
Test for Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.34636450813867986, 'r': 0.29610233370986844, 'f1': 0.3192673379969098}, 'combined': 0.1982818204401861, 'stategy': 1, 'epoch': 6}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 6}
******************************
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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2023-01-22 20:14:10.548607: step: 2120/530, loss: 5.6259759730892256e-05
==================================================
Loss: 0.004
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3383751927029805, 'r': 0.3569954594741122, 'f1': 0.34743602427120435}, 'combined': 0.2560054915682558, 'stategy': 1, 'epoch': 15}
Test Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33633573932523514, 'r': 0.2907590862218155, 'f1': 0.31189117896048324}, 'combined': 0.19370083745966854, 'stategy': 1, 'epoch': 15}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34571716045783474, 'r': 0.3417811017049941, 'f1': 0.3437378637376563}, 'combined': 0.2532805311751152, 'stategy': 1, 'epoch': 15}
Test Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.319360688989712, 'r': 0.28808798057232177, 'f1': 0.30291934306668467}, 'combined': 0.18812885516773048, 'stategy': 1, 'epoch': 15}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3439301841882813, 'r': 0.3308778052817052, 'f1': 0.3372777628306743}, 'combined': 0.2485204568226021, 'stategy': 1, 'epoch': 15}
Test Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3495400277691648, 'r': 0.2988170424956533, 'f1': 0.3221944268739721}, 'combined': 0.2000996966901511, 'stategy': 1, 'epoch': 15}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 15}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.38636363636363635, 'r': 0.5543478260869565, 'f1': 0.4553571428571429}, 'combined': 0.22767857142857145, 'stategy': 1, 'epoch': 15}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 15}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34038473737630043, 'r': 0.36363682569802114, 'f1': 0.35162680209698555}, 'combined': 0.2590934331240946, 'stategy': 1, 'epoch': 3}
Test for Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33394714661777014, 'r': 0.29100636614268593, 'f1': 0.31100151815037574}, 'combined': 0.19314831127233864, 'stategy': 1, 'epoch': 3}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 3}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3504253708142644, 'r': 0.3417811017049941, 'f1': 0.3460492614765262}, 'combined': 0.25498366635112457, 'stategy': 1, 'epoch': 4}
Test for Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3179244390742019, 'r': 0.28710683766048106, 'f1': 0.3017307826140814}, 'combined': 0.18739069657085058, 'stategy': 1, 'epoch': 4}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3984375, 'r': 0.5543478260869565, 'f1': 0.4636363636363637}, 'combined': 0.23181818181818184, 'stategy': 1, 'epoch': 4}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3447809032815958, 'r': 0.33562163829878294, 'f1': 0.34013962189126656}, 'combined': 0.25062919507777537, 'stategy': 1, 'epoch': 6}
Test for Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.34636450813867986, 'r': 0.29610233370986844, 'f1': 0.3192673379969098}, 'combined': 0.1982818204401861, 'stategy': 1, 'epoch': 6}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 6}
******************************
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
2023-01-22 20:16:40.536916: step: 4/530, loss: 0.002954723546281457
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2023-01-22 20:16:42.696085: step: 12/530, loss: 0.0005227226647548378
2023-01-22 20:16:43.783381: step: 16/530, loss: 2.8173930331831798e-05
2023-01-22 20:16:44.856205: step: 20/530, loss: 0.0005631744861602783
2023-01-22 20:16:45.951889: step: 24/530, loss: 0.007691206876188517
2023-01-22 20:16:47.056065: step: 28/530, loss: 0.0017431259620934725
2023-01-22 20:16:48.134879: step: 32/530, loss: 0.0006445638136938214
2023-01-22 20:16:49.211029: step: 36/530, loss: 0.0014654003316536546
2023-01-22 20:16:50.290036: step: 40/530, loss: 0.00032433445448987186
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2023-01-22 20:26:15.750694: step: 2120/530, loss: 0.002124864375218749
==================================================
Loss: 0.003
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3405777502606882, 'r': 0.35414915966386556, 'f1': 0.3472308970099668}, 'combined': 0.2558543451652387, 'stategy': 1, 'epoch': 16}
Test Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33241601408594385, 'r': 0.28408252835831405, 'f1': 0.3063545985816058}, 'combined': 0.19026232964541834, 'stategy': 1, 'epoch': 16}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3489662268453928, 'r': 0.3403579517998708, 'f1': 0.3446083392863245}, 'combined': 0.2539219342109759, 'stategy': 1, 'epoch': 16}
Test Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3189445718257113, 'r': 0.28487334160100625, 'f1': 0.30094769943429184}, 'combined': 0.18690436070129704, 'stategy': 1, 'epoch': 16}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3418776959829853, 'r': 0.3256595889629196, 'f1': 0.3335716295110954}, 'combined': 0.24578962174501767, 'stategy': 1, 'epoch': 16}
Test Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.34412984191639157, 'r': 0.29215366638509055, 'f1': 0.3160188617705251}, 'combined': 0.19626434573116822, 'stategy': 1, 'epoch': 16}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 16}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.38636363636363635, 'r': 0.5543478260869565, 'f1': 0.4553571428571429}, 'combined': 0.22767857142857145, 'stategy': 1, 'epoch': 16}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 16}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34038473737630043, 'r': 0.36363682569802114, 'f1': 0.35162680209698555}, 'combined': 0.2590934331240946, 'stategy': 1, 'epoch': 3}
Test for Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33394714661777014, 'r': 0.29100636614268593, 'f1': 0.31100151815037574}, 'combined': 0.19314831127233864, 'stategy': 1, 'epoch': 3}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 3}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3504253708142644, 'r': 0.3417811017049941, 'f1': 0.3460492614765262}, 'combined': 0.25498366635112457, 'stategy': 1, 'epoch': 4}
Test for Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3179244390742019, 'r': 0.28710683766048106, 'f1': 0.3017307826140814}, 'combined': 0.18739069657085058, 'stategy': 1, 'epoch': 4}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3984375, 'r': 0.5543478260869565, 'f1': 0.4636363636363637}, 'combined': 0.23181818181818184, 'stategy': 1, 'epoch': 4}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3447809032815958, 'r': 0.33562163829878294, 'f1': 0.34013962189126656}, 'combined': 0.25062919507777537, 'stategy': 1, 'epoch': 6}
Test for Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.34636450813867986, 'r': 0.29610233370986844, 'f1': 0.3192673379969098}, 'combined': 0.1982818204401861, 'stategy': 1, 'epoch': 6}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 6}
******************************
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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2023-01-22 20:38:18.576588: step: 2120/530, loss: 0.009466171264648438
==================================================
Loss: 0.003
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3379255524152107, 'r': 0.35652107617240447, 'f1': 0.34697434375412217}, 'combined': 0.25566530592409, 'stategy': 1, 'epoch': 17}
Test Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3293799270517206, 'r': 0.28507164804179574, 'f1': 0.30562824620387646}, 'combined': 0.18981122658977592, 'stategy': 1, 'epoch': 17}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34955593205092494, 'r': 0.3455761681186563, 'f1': 0.347554657630786}, 'combined': 0.25609290562268444, 'stategy': 1, 'epoch': 17}
Test Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3195131300644146, 'r': 0.2875934207305809, 'f1': 0.3027141575831518}, 'combined': 0.1880014241832206, 'stategy': 1, 'epoch': 17}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34331208590783885, 'r': 0.32898027207487407, 'f1': 0.33599341740980354}, 'combined': 0.2475740970388026, 'stategy': 1, 'epoch': 17}
Test Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.34614247010149396, 'r': 0.2965959171254657, 'f1': 0.3194595045700125}, 'combined': 0.19840116599611304, 'stategy': 1, 'epoch': 17}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3138888888888889, 'r': 0.4035714285714286, 'f1': 0.35312499999999997}, 'combined': 0.23541666666666664, 'stategy': 1, 'epoch': 17}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.36363636363636365, 'r': 0.5217391304347826, 'f1': 0.42857142857142855}, 'combined': 0.21428571428571427, 'stategy': 1, 'epoch': 17}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 17}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34038473737630043, 'r': 0.36363682569802114, 'f1': 0.35162680209698555}, 'combined': 0.2590934331240946, 'stategy': 1, 'epoch': 3}
Test for Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33394714661777014, 'r': 0.29100636614268593, 'f1': 0.31100151815037574}, 'combined': 0.19314831127233864, 'stategy': 1, 'epoch': 3}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 3}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3504253708142644, 'r': 0.3417811017049941, 'f1': 0.3460492614765262}, 'combined': 0.25498366635112457, 'stategy': 1, 'epoch': 4}
Test for Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3179244390742019, 'r': 0.28710683766048106, 'f1': 0.3017307826140814}, 'combined': 0.18739069657085058, 'stategy': 1, 'epoch': 4}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3984375, 'r': 0.5543478260869565, 'f1': 0.4636363636363637}, 'combined': 0.23181818181818184, 'stategy': 1, 'epoch': 4}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3447809032815958, 'r': 0.33562163829878294, 'f1': 0.34013962189126656}, 'combined': 0.25062919507777537, 'stategy': 1, 'epoch': 6}
Test for Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.34636450813867986, 'r': 0.29610233370986844, 'f1': 0.3192673379969098}, 'combined': 0.1982818204401861, 'stategy': 1, 'epoch': 6}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 6}
******************************
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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2023-01-22 20:50:23.291365: step: 2120/530, loss: 0.002885560505092144
==================================================
Loss: 0.003
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34103149757219525, 'r': 0.36173929249119, 'f1': 0.35108030781373323}, 'combined': 0.25869075312590867, 'stategy': 1, 'epoch': 18}
Test Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33559104517907207, 'r': 0.2877917271713704, 'f1': 0.3098588244624659}, 'combined': 0.19243863835037356, 'stategy': 1, 'epoch': 18}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34955593205092494, 'r': 0.3455761681186563, 'f1': 0.347554657630786}, 'combined': 0.25609290562268444, 'stategy': 1, 'epoch': 18}
Test Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3206897121656956, 'r': 0.2883352604931922, 'f1': 0.303653071206893}, 'combined': 0.1885845389600704, 'stategy': 1, 'epoch': 18}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34647341941162424, 'r': 0.331352188583413, 'f1': 0.3387441384742165}, 'combined': 0.24960094413889639, 'stategy': 1, 'epoch': 18}
Test Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3457871409331371, 'r': 0.2956087502942712, 'f1': 0.3187351400192621}, 'combined': 0.19795129748564702, 'stategy': 1, 'epoch': 18}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 18}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.36363636363636365, 'r': 0.5217391304347826, 'f1': 0.42857142857142855}, 'combined': 0.21428571428571427, 'stategy': 1, 'epoch': 18}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 18}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34038473737630043, 'r': 0.36363682569802114, 'f1': 0.35162680209698555}, 'combined': 0.2590934331240946, 'stategy': 1, 'epoch': 3}
Test for Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33394714661777014, 'r': 0.29100636614268593, 'f1': 0.31100151815037574}, 'combined': 0.19314831127233864, 'stategy': 1, 'epoch': 3}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 3}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3504253708142644, 'r': 0.3417811017049941, 'f1': 0.3460492614765262}, 'combined': 0.25498366635112457, 'stategy': 1, 'epoch': 4}
Test for Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3179244390742019, 'r': 0.28710683766048106, 'f1': 0.3017307826140814}, 'combined': 0.18739069657085058, 'stategy': 1, 'epoch': 4}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3984375, 'r': 0.5543478260869565, 'f1': 0.4636363636363637}, 'combined': 0.23181818181818184, 'stategy': 1, 'epoch': 4}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3447809032815958, 'r': 0.33562163829878294, 'f1': 0.34013962189126656}, 'combined': 0.25062919507777537, 'stategy': 1, 'epoch': 6}
Test for Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.34636450813867986, 'r': 0.29610233370986844, 'f1': 0.3192673379969098}, 'combined': 0.1982818204401861, 'stategy': 1, 'epoch': 6}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 6}
******************************
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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2023-01-22 21:02:06.535438: step: 2040/530, loss: 0.0005633449763990939
2023-01-22 21:02:07.618209: step: 2044/530, loss: 0.0005284935468807817
2023-01-22 21:02:08.718739: step: 2048/530, loss: 0.007640881463885307
2023-01-22 21:02:09.800528: step: 2052/530, loss: 0.0004758746363222599
2023-01-22 21:02:10.904476: step: 2056/530, loss: 0.0018036675173789263
2023-01-22 21:02:11.996063: step: 2060/530, loss: 0.001490497961640358
2023-01-22 21:02:13.078659: step: 2064/530, loss: 0.004089886322617531
2023-01-22 21:02:14.152021: step: 2068/530, loss: 0.005039777606725693
2023-01-22 21:02:15.240563: step: 2072/530, loss: 0.0077629066072404385
2023-01-22 21:02:16.309370: step: 2076/530, loss: 0.00040686584543436766
2023-01-22 21:02:17.421030: step: 2080/530, loss: 1.979967964871321e-05
2023-01-22 21:02:18.510144: step: 2084/530, loss: 0.0017287888331338763
2023-01-22 21:02:19.591515: step: 2088/530, loss: 7.450580152834618e-10
2023-01-22 21:02:20.669741: step: 2092/530, loss: 9.442597729503177e-06
2023-01-22 21:02:21.752985: step: 2096/530, loss: 0.0002690191613510251
2023-01-22 21:02:22.820189: step: 2100/530, loss: 0.00023467732535209507
2023-01-22 21:02:23.928690: step: 2104/530, loss: 0.0056996820494532585
2023-01-22 21:02:25.045103: step: 2108/530, loss: 0.003091799793764949
2023-01-22 21:02:26.108866: step: 2112/530, loss: 0.013210326433181763
2023-01-22 21:02:27.204568: step: 2116/530, loss: 0.002925982465967536
2023-01-22 21:02:28.306329: step: 2120/530, loss: 2.0695311775398295e-07
==================================================
Loss: 0.003
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34439964742752677, 'r': 0.35746984277582, 'f1': 0.35081304868316043}, 'combined': 0.2584938253454866, 'stategy': 1, 'epoch': 19}
Test Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3329017636857124, 'r': 0.2868026074878887, 'f1': 0.30813755172184426}, 'combined': 0.1913696373851454, 'stategy': 1, 'epoch': 19}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.35111937125695253, 'r': 0.33846041859303966, 'f1': 0.34467370163967515}, 'combined': 0.2539700959450238, 'stategy': 1, 'epoch': 19}
Test Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3227399871223643, 'r': 0.2885825404140626, 'f1': 0.30470699567479614}, 'combined': 0.1892390815243471, 'stategy': 1, 'epoch': 19}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3437452067669172, 'r': 0.32613397226462737, 'f1': 0.3347080883806399}, 'combined': 0.24662701249099783, 'stategy': 1, 'epoch': 19}
Test Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3464304885346737, 'r': 0.2968427088332643, 'f1': 0.31972532062530223}, 'combined': 0.19856625175676668, 'stategy': 1, 'epoch': 19}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 19}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3870967741935484, 'r': 0.5217391304347826, 'f1': 0.4444444444444444}, 'combined': 0.2222222222222222, 'stategy': 1, 'epoch': 19}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 19}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.34038473737630043, 'r': 0.36363682569802114, 'f1': 0.35162680209698555}, 'combined': 0.2590934331240946, 'stategy': 1, 'epoch': 3}
Test for Chinese: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.33394714661777014, 'r': 0.29100636614268593, 'f1': 0.31100151815037574}, 'combined': 0.19314831127233864, 'stategy': 1, 'epoch': 3}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3210227272727273, 'r': 0.4035714285714286, 'f1': 0.3575949367088607}, 'combined': 0.23839662447257381, 'stategy': 1, 'epoch': 3}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3504253708142644, 'r': 0.3417811017049941, 'f1': 0.3460492614765262}, 'combined': 0.25498366635112457, 'stategy': 1, 'epoch': 4}
Test for Korean: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.3179244390742019, 'r': 0.28710683766048106, 'f1': 0.3017307826140814}, 'combined': 0.18739069657085058, 'stategy': 1, 'epoch': 4}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3984375, 'r': 0.5543478260869565, 'f1': 0.4636363636363637}, 'combined': 0.23181818181818184, 'stategy': 1, 'epoch': 4}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3447809032815958, 'r': 0.33562163829878294, 'f1': 0.34013962189126656}, 'combined': 0.25062919507777537, 'stategy': 1, 'epoch': 6}
Test for Russian: {'template': {'p': 0.9516129032258065, 'r': 0.4609375, 'f1': 0.6210526315789474}, 'slot': {'p': 0.34636450813867986, 'r': 0.29610233370986844, 'f1': 0.3192673379969098}, 'combined': 0.1982818204401861, 'stategy': 1, 'epoch': 6}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5514705882352942, 'r': 0.3232758620689655, 'f1': 0.4076086956521739}, 'combined': 0.2717391304347826, 'stategy': 1, 'epoch': 6}