docstr: Object represent a mention
  
  Args:
      topic_id: str topic ID
      doc_id: str document ID
      sent_id: int sentence number
      tokens_numbers: List[int] - tokens numbers
      mention_context: List[str] - list of tokens strings
      coref_chain: str
      mention_type: str one of (HUM/NON/TIM/LOC/ACT/NEG)
      is_continuous: bool
      is_singleton: bool
      score: float
      predicted_coref_chain: str (should be field while evaluated)
      mention_pos: str
      mention_ner: str
      mention_index: in case order is of value (default = -1)
docstr_tok: ["Object","represent","a","mention","Args",":","topic_id",":","str","topic","ID","doc_id",":","str","document","ID","sent_id",":","int","sentence","number","tokens_numbers",":","List","[","int","]","-","tokens","numbers","mention_context",":","List","[","str","]","-","list","of","tokens","strings","coref_chain",":","str","mention_type",":","str","one","of","(","HUM\/NON\/TIM\/LOC\/ACT\/NEG",")","is_continuous",":","bool","is_singleton",":","bool","score",":","float","predicted_coref_chain",":","str","(","should","be","field","while","evaluated",")","mention_pos",":","str","mention_ner",":","str","mention_index",":","in","case","order","is","of","value","(","default","=","-1",")"]
code: def __init__(
      self,
      topic_id: str,
      doc_id: str,
      sent_id: int,
      tokens_numbers: List[int],
      tokens_str: str,
      mention_context: List[str],
      mention_head: str,
      mention_head_lemma: str,
      coref_chain: str,
      mention_type: str = "NA",
      is_continuous: bool = True,
      is_singleton: bool = False,
      score: float = float(-1),
      predicted_coref_chain: str = None,
      mention_pos: str = None,
      mention_ner: str = None,
      mention_index: int = -1,
  ) -> None:
      """
      Object represent a mention
  
      Args:
          topic_id: str topic ID
          doc_id: str document ID
          sent_id: int sentence number
          tokens_numbers: List[int] - tokens numbers
          mention_context: List[str] - list of tokens strings
          coref_chain: str
          mention_type: str one of (HUM/NON/TIM/LOC/ACT/NEG)
          is_continuous: bool
          is_singleton: bool
          score: float
          predicted_coref_chain: str (should be field while evaluated)
          mention_pos: str
          mention_ner: str
          mention_index: in case order is of value (default = -1)
      """
      super(MentionData, self).__init__(
          tokens_str,
          mention_context,
          mention_head,
          mention_head_lemma,
          mention_pos,
          mention_ner,
      )
      self.topic_id = topic_id
      self.doc_id = doc_id
      self.sent_id = sent_id
      self.tokens_number = tokens_numbers
      self.mention_type = mention_type
      self.coref_chain = coref_chain
      self.is_continuous = is_continuous
      self.is_singleton = is_singleton
      self.score = score
      self.predicted_coref_chain = predicted_coref_chain
      self.mention_id = self.gen_mention_id()
      self.mention_index = mention_index
  
file_path: nlp-architect/nlp_architect/common/cdc/mention_data.py
imports: import sys
  from typing import List
  from nlp_architect.utils.io import load_json_file
  from nlp_architect.utils.string_utils import StringUtils
label_desc: Use image node_id 1 to create a new MentionData object from inherited base classes: MentionDataLight with example: obj = MentionData(topic_id, doc_id, sent_id, tokens_numbers, tokens_str, mention_context, mention_head, mention_head_lemma, coref_chain, mention_type, is_continuous, is_singleton, score, predicted_coref_chain, mention_pos, mention_ner, mention_index)