class_docstr: Detect phrases based on collocation counts.
class_docstr_tok: ["Detect","phrases","based","on","collocation","counts","."]
docstr: Extract all found phrases.
  
  Returns
  ------
  dict(str, float)
      Mapping between phrases and their scores.
docstr_tok: ["Extract","all","found","phrases",".","Returns","--","--","--","dict","(","str",",","float",")","Mapping","between","phrases","and","their","scores","."]
code: def export_phrases(self):
      """Extract all found phrases.
  
      Returns
      ------
      dict(str, float)
          Mapping between phrases and their scores.
  
      """
      result, source_vocab = {}, self.vocab
      for token in source_vocab:
          unigrams = token.split(self.delimiter)
          if len(unigrams) < 2:
              continue  # no phrases here
          phrase, score = self.score_candidate(
              unigrams[0], unigrams[-1], unigrams[1:-1]
          )
          if score is not None:
              result[phrase] = score
      return result
  
imports: import logging
  import itertools
  from math import log
  import pickle
  from inspect import getfullargspec
  import time
  from gensim import utils, interfaces
label_desc: Use image node_id 7 for calling the Phrases obj's underlying member method code with example usage: obj.export_phrases() and returns: result