docstr: Cross Document co-reference sieve system resources configuration class
docstr_tok: ["Cross","Document","co-reference","sieve","system","resources","configuration","class"]
code: def __init__(self):
      """Cross Document co-reference sieve system resources configuration class"""
  
      self.__eval_output_dir = str(
          LIBRARY_ROOT / "datasets" / "cdc" / "test_predict"
      )
  
      self.__elastic_index = "enwiki_v2"
      self.__elastic_host = "localhost"
      self.__elastic_port = 9200
      self.__wiki_folder = str(LIBRARY_ROOT / "dumps" "wikipedia")
      self.__wd_file = str(
          LIBRARY_ROOT
          / "dump"
          / "within_doc_core"
          / "ecb_wd_coref_proc.json"
      )
      self.__wn_folder = str(LIBRARY_ROOT / "dump" / "wordnet")
      self.__elmo_file = str(
          LIBRARY_ROOT
          / "dump"
          / "embedde"
          / "ecb_all_with_stop_elmo.pickle"
      )
      self.__glove_file = str(
          LIBRARY_ROOT
          / "dump"
          / "embedde"
          / "ecb_all_embed_glove.pickle"
      )
      self.__referent_dict_file = str(
          LIBRARY_ROOT / "dataset" / "coref.dict1.tsv"
      )
      self.__vo_dict_file = str(
          LIBRARY_ROOT
          / "dataset"
          / "verbocean.unrefined.2004-05-20.txt"
      )
  
      self.__wiki_search_method = WikipediaSearchMethod.ONLINE
      self.__wn_search_method = OnlineOROfflineMethod.ONLINE
      self.__embed_search_method = EmbeddingMethod.ELMO
      self.__referent_dict_method = OnlineOROfflineMethod.ONLINE
      self.__vo_search_method = OnlineOROfflineMethod.ONLINE
  
imports: from nlp_architect import LIBRARY_ROOT
  from nlp_architect.data.cdc_resources.relations.relation_types_enums import WikipediaSearchMethod, OnlineOROfflineMethod, EmbeddingMethod
label_desc: Use image node_id 1 to create a new SievesResources object from inherited base classes: object with example: obj = SievesResources()