class_docstr: Compute the Sorensen distance between the two sequences.
  They should contain hashable items.
  The return value is a float between 0 and 1, where 0 means equal,
  and 1 totally different.
  
  https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient
  https://github.com/Yomguithereal/talisman/blob/master/src/metrics/dice.js
class_docstr_tok: ["Compute","the","Sorensen","distance","between","the","two","sequences",".","They","should","contain","hashable","items",".","The","return","value","is","a","float","between","0","and","1",",","where","0","means","equal",",","and","1","totally","different",".","https",":","\/\/en.wikipedia.org\/wiki\/S","%","C3","%","B8rensen","%","E2","%","80","%","93Dice_coefficient","https",":","\/\/github.com\/Yomguithereal\/talisman\/blob\/master\/src\/metrics\/dice.js"]
code: def __init__(
      self, qval: int = 1, as_set: bool = False, external: bool = True
  ) -> None:
      self.qval = qval
      self.as_set = as_set
      self.external = external
  
imports: from __future__ import annotations
  from functools import reduce
  from itertools import islice, permutations, repeat
  from math import log
  from typing import Sequence
  from .base import Base, BaseSimilarity
  from .edit_based import DamerauLevenshtein
label_desc: Use image node_id 1 to create a new Sorensen object from inherited base classes: _BaseSimilarity with example: obj = Sorensen(qval, as_set, external)