docstr: Sents: List of plain text consequtive sentences. 
  Returns a dictionary consisting of a list of sentences and a list of mentions, where for each mention AT LEAST (it may give additional information) the following information is given:
      sent_idx - the index of the sentence that contains the mention
      text - the textual span that we hypothesise that represents an entity
      start_pos - the character idx at which the textual mention starts 
      end_pos - the character idx at which the mention ends
code: def predict(self, sents):
      """Sents: List of plain text consequtive sentences.
      Returns a dictionary consisting of a list of sentences and a list of mentions, where for each mention AT LEAST (it may give additional information) the following information is given:
          sent_idx - the index of the sentence that contains the mention
          text - the textual span that we hypothesise that represents an entity
          start_pos - the character idx at which the textual mention starts
          end_pos - the character idx at which the mention ends"""
      pass
  
file_path: BLINK/blink/ner.py
imports: from flair.models import SequenceTagger
  from flair.data import Sentence
label_desc: Use image node_id 2 for calling the NER_model obj's underlying member method code with example usage: obj.predict(sents) without return types