class_docstr: Text spans scoring class.
  Contains misc scoring algorithms for scoring text fragments extracted
  from a corpus.
  
  Arguments:
      documents(list): List of spaCy documents.
      spans(list[list]): List of spaCy spans representing noun phrases of documents
          document.
class_docstr_tok: ["Text","spans","scoring","class",".","Contains","misc","scoring","algorithms","for","scoring","text","fragments","extracted","from","a","corpus",".","Arguments",":","documents","(","list",")",":","List","of","spaCy","documents",".","spans","(","list","[","list","]",")",":","List","of","spaCy","spans","representing","noun","phrases","of","documents","document","."]
code: def re_index_min_tf(self, tf):
      filtered_doc_text_spans = []
      for d in self.doc_text_spans:
          filtered_doc_phrases = [
              p for p in d if self.index.tf(p) >= tf
          ]
          filtered_doc_text_spans.append(filtered_doc_phrases)
      self._doc_text_spans = filtered_doc_text_spans
      self.index = CorpusIndex(self.documents, self.doc_text_spans)
  
imports: import itertools
  import math
  import numpy
  import logging
  from spacy.tokens.doc import Doc
  from spacy.tokens.span import Span
  from spacy.tokens.token import Token
label_desc: Use image node_id 2 for calling the TextSpanScoring obj's underlying member method code with example usage: obj.re_index_min_tf(tf) without return types