class_docstr: A `Splitter` that uses a state machine to determine sentence breaks.
  
  `StateBasedSentenceBreaker` splits text into sentences by using a state
  machine to determine when a sequence of characters indicates a potential
  sentence break.
  
  The state machine consists of an `initial state`, then transitions to a
  `collecting terminal punctuation state` once an acronym, an emoticon, or
  terminal punctuation (ellipsis, question mark, exclamation point, etc.), is
  encountered.
  
  It transitions to the `collecting close punctuation state` when a close
  punctuation (close bracket, end quote, etc.) is found.
  
  If non-punctuation is encountered in the collecting terminal punctuation or
  collecting close punctuation states, then the state machine exits, returning
  false, indicating it has moved past the end of a potential sentence fragment.
class_docstr_tok: ["A","`","Splitter","`","that","uses","a","state","machine","to","determine","sentence","breaks",".","`","StateBasedSentenceBreaker","`","splits","text","into","sentences","by","using","a","state","machine","to","determine","when","a","sequence","of","characters","indicates","a","potential","sentence","break",".","The","state","machine","consists","of","an","`","initial","state","`",",","then","transitions","to","a","`","collecting","terminal","punctuation","state","`","once","an","acronym",",","an","emoticon",",","or","terminal","punctuation","(","ellipsis",",","question","mark",",","exclamation","point",",","etc",".",")",",","is","encountered",".","It","transitions","to","the","`","collecting","close","punctuation","state","`","when","a","close","punctuation","(","close","bracket",",","end","quote",",","etc",".",")","is","found",".","If","non-punctuation","is","encountered","in","the","collecting","terminal","punctuation","or","collecting","close","punctuation","states",",","then","the","state","machine","exits",",","returning","false",",","indicating","it","has","moved","past","the","end","of","a","potential","sentence","fragment","."]
docstr: Splits `doc` into sentence fragments and returns the fragments' text.
  
  Args:
    doc: A string `Tensor` of shape [batch] with a batch of documents.
  
  Returns:
    results: A string `RaggedTensor` of shape [batch, (num_sentences)]
    with each input broken up into its constituent sentence fragments.
docstr_tok: ["Splits","`","doc","`","into","sentence","fragments","and","returns","the","fragments","'","text",".","Args",":","doc",":","A","string","`","Tensor","`","of","shape","[","batch","]","with","a","batch","of","documents",".","Returns",":","results",":","A","string","`","RaggedTensor","`","of","shape","[","batch",",","(","num_sentences",")","]","with","each","input","broken","up","into","its","constituent","sentence","fragments","."]
code: def break_sentences(self, doc):
      """Splits `doc` into sentence fragments and returns the fragments' text.
  
      Args:
        doc: A string `Tensor` of shape [batch] with a batch of documents.
  
      Returns:
        results: A string `RaggedTensor` of shape [batch, (num_sentences)]
        with each input broken up into its constituent sentence fragments.
  
      """
      results, _, _ = self.break_sentences_with_offsets(doc)
      return results
  
file_path: text/tensorflow_text/python/ops/state_based_sentence_breaker_op.py
imports: from tensorflow.python.framework import dtypes
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.framework import load_library
  from tensorflow.python.platform import resource_loader
  from tensorflow_text.python.ops import byte_splitter
  from tensorflow_text.python.ops import sentence_breaking_ops
label_desc: Use image node_id 1 for calling the StateBasedSentenceBreaker obj's underlying member method code with example usage: obj.break_sentences(doc) and returns: results