class_docstr: XlnetForTokenClassification can load XLNet Models with a token
  classification head on top (a linear layer on top of the hidden-states
  output) e.g. for Named-Entity-Recognition (NER) tasks.
  
  Pretrained models can be loaded with :meth:`.pretrained` of the companion
  object:
  
  >>> token_classifier = XlnetForTokenClassification.pretrained() \
  ...     .setInputCols(["token", "document"]) \
  ...     .setOutputCol("label")
  
  The default model is ``"xlnet_base_token_classifier_conll03"``, if no name is
  provided.
  
  For available pretrained models please see the `Models Hub
  <https://sparknlp.org/models?task=Named+Entity+Recognition>`__.
  
  To see which models are compatible and how to import them see
  `Import Transformers into Spark NLP 🚀
  <https://github.com/JohnSnowLabs/spark-nlp/discussions/5669>`_.
  
  ====================== ======================
  Input Annotation types Output Annotation type
  ====================== ======================
  ``DOCUMENT, TOKEN``    ``NAMED_ENTITY``
  ====================== ======================
  
  Parameters
  ----------
  batchSize
      Batch size. Large values allows faster processing but requires more
      memory, by default 8
  caseSensitive
      Whether to ignore case in tokens for embeddings matching, by default
      True
  configProtoBytes
      ConfigProto from tensorflow, serialized into byte array.
  maxSentenceLength
      Max sentence length to process, by default 128
  
  Examples
  --------
  >>> import sparknlp
  >>> from sparknlp.base import *
  >>> from sparknlp.annotator import *
  >>> from pyspark.ml import Pipeline
  >>> documentAssembler = DocumentAssembler() \
  ...     .setInputCol("text") \
  ...     .setOutputCol("document")
  >>> tokenizer = Tokenizer() \
  ...     .setInputCols(["document"]) \
  ...     .setOutputCol("token")
  >>> tokenClassifier = XlnetForTokenClassification.pretrained() \
  ...     .setInputCols(["token", "document"]) \
  ...     .setOutputCol("label") \
  ...     .setCaseSensitive(True)
  >>> pipeline = Pipeline().setStages([
  ...     documentAssembler,
  ...     tokenizer,
  ...     tokenClassifier
  ... ])
  >>> data = spark.createDataFrame([["John Lenon was born in London and lived in Paris. My name is Sarah and I live in London"]]).toDF("text")
  >>> result = pipeline.fit(data).transform(data)
  >>> result.select("label.result").show(truncate=False)
  +------------------------------------------------------------------------------------+
  |result                                                                              |
  +------------------------------------------------------------------------------------+
  |[B-PER, I-PER, O, O, O, B-LOC, O, O, O, B-LOC, O, O, O, O, B-PER, O, O, O, O, B-LOC]|
  +------------------------------------------------------------------------------------+
docstr: Returns labels used to train this model
code: def getClasses(self):
      """
      Returns labels used to train this model
      """
      return self._call_java("getClasses")
  
file_path: spark-nlp/python/sparknlp/annotator/classifier_dl/xlnet_for_token_classification.py
imports: from sparknlp.common import *
label_desc: Use image node_id 1 for calling the XlnetForTokenClassification obj's underlying member method code with example usage: obj.getClasses() and returns: self