class_docstr: A `SentenceSplitter` that uses spaCy's built-in sentence boundary detection.
  
  Spacy's default sentence splitter uses a dependency parse to detect sentence boundaries, so
  it is slow, but accurate.
  
  Another option is to use rule-based sentence boundary detection. It's fast and has a small memory footprint,
  since it uses punctuation to detect sentence boundaries. This can be activated with the `rule_based` flag.
  
  By default, `SpacySentenceSplitter` calls the default spacy boundary detector.
  
  Registered as a `SentenceSplitter` with name "spacy".
docstr: This method lets you take advantage of spacy's batch processing.
docstr_tok: ["This","method","lets","you","take","advantage","of","spacy","'s","batch","processing","."]
code: def batch_split_sentences(self, texts: List[str]) -> List[List[str]]:
      """
      This method lets you take advantage of spacy's batch processing.
      """
      if self._is_version_3:
          return [
              [sentence.text.strip() for sentence in doc.sents]
              for doc in self.spacy.pipe(texts)
          ]
      return [
          [sentence.string.strip() for sentence in doc.sents]
          for doc in self.spacy.pipe(texts)
      ]
  
file_path: allennlp/allennlp/data/tokenizers/sentence_splitter.py
imports: from typing import List, Dict, Any
  import spacy
  from allennlp.common import Registrable
  from allennlp.common.util import get_spacy_model
label_desc: Use image node_id 3 for calling the SpacySentenceSplitter obj's underlying member method code with example usage: obj.batch_split_sentences(texts) and returns: unknown, unknown