text: 
  # best config
  best_config = {
      "embedding_dim": 768,
      "max_span_width": 20,
      # max training sentences depends on size of memery 
      "max_training_sentences": 11,
      # max seq length
      "max_seq_length": 128,
      "bert_max_seq_length": 512,
  
      "device": "cuda",
      "checkpoint_path": "./data/checkpoint",
      "lr": 0.0002,
      "weight_decay": 0.0005,
      "dropout": 0.3,
  
      "report_frequency": 1000,
      "eval_frequency": 5000,
  
      # ontonotes dir
      "ontonotes_root_dir": "./data/ontonotes",
      "train_file_path": "./data/train.json",
      "test_file_path": "./data/test.json",
      "val_file_path": "./data/val.json",
  
      # max candidate mentions size in first/second stage
      "top_unit_ratio": 0.5,
      "max_top_antecedents": 50,
      # use coarse to fine pruning
      "coarse_to_fine": True,
      # high order coref depth
      "coref_depth": 2,
  
      # FFNN config
      "ffnn_depth": 1,
      "ffnn_size": 3000,
  
      # use span features, such as distance
      "use_features": True,
      "feature_dim": 20,
      "model_heads": True,
      # use metadata, such as genre and speaker info
      "use_metadata": True,
      "genres": ["bc", "bn", "mz", "nw", "tc", "wb"],
  
      # 选择topk时是否考虑单元互斥
      "extract_units": True,
  
      # 指代检测损失权重
      "unit_detection_loss_weight": 0.2,
  
      # interaction among units
      "use_units_interaction_before_score": True,        # 计算得分前的交互
      "use_units_interaction_after_score" : True,         # 计算得分后的交互
      "interaction_method_after_score": "max",           # 计算得分后的交互方式max/mean
  
      # 带权self attention
      "wsa_depth": 1,
      "wsa_layer_num": 1,
      "wsa_dropout": 0.3,
      "wsa_pwff_size": 3072,
      "wsa_head_num": 8,
      "wsa_lr": 0.0002,
  
  
      # transformer model
      "transformer_model_name": './data/bert-base-chinese',
      "transformer_lr": 0.00001,
  }
  