class_docstr: DeBertaForSequenceClassification can load DeBERTa v2 & v3 Models with sequence classification/regression head on
  top (a linear layer on top of the pooled output) e.g. for multi-class document classification tasks.
  
  Pretrained models can be loaded with :meth:`.pretrained` of the companion
  object:
  
  >>> sequenceClassifier = DeBertaForSequenceClassification.pretrained() \
  ...     .setInputCols(["token", "document"]) \
  ...     .setOutputCol("label")
  
  The default model is ``"deberta_v3_xsmall_sequence_classifier_imdb"``, if no name is
  provided.
  
  For available pretrained models please see the `Models Hub
  <https://sparknlp.org/models?task=Text+Classification>`__.
  
  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``    ``CATEGORY``
  ====================== ======================
  
  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
  coalesceSentences
      Instead of 1 class per sentence (if inputCols is `sentence`) output
      1 class per document by averaging probabilities in all sentences, by
      default False.
  
  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")
  >>> sequenceClassifier = DeBertaForSequenceClassification.pretrained() \
  ...     .setInputCols(["token", "document"]) \
  ...     .setOutputCol("label") \
  ...     .setCaseSensitive(True)
  >>> pipeline = Pipeline().setStages([
  ...     documentAssembler,
  ...     tokenizer,
  ...     sequenceClassifier
  ... ])
  >>> data = spark.createDataFrame([["I loved this movie when I was a child.", "It was pretty boring."]]).toDF("text")
  >>> result = pipeline.fit(data).transform(data)
  >>> result.select("label.result").show(truncate=False)
  +------+
  |result|
  +------+
  |[pos] |
  |[neg] |
  +------+
docstr: Downloads and loads a pretrained model.
  
  Parameters
  ----------
  name : str, optional
      Name of the pretrained model, by default
      "deberta_v3_xsmall_sequence_classifier_imdb"
  lang : str, optional
      Language of the pretrained model, by default "en"
  remote_loc : str, optional
      Optional remote address of the resource, by default None. Will use
      Spark NLPs repositories otherwise.
  
  Returns
  -------
  AlbertForSequenceClassification
      The restored model
docstr_tok: ["Downloads","and","loads","a","pretrained","model",".","Parameters","--","--","--","--","--","name",":","str",",","optional","Name","of","the","pretrained","model",",","by","default","``","deberta_v3_xsmall_sequence_classifier_imdb","''","lang",":","str",",","optional","Language","of","the","pretrained","model",",","by","default","``","en","''","remote_loc",":","str",",","optional","Optional","remote","address","of","the","resource",",","by","default","None",".","Will","use","Spark","NLPs","repositories","otherwise",".","Returns","--","--","--","-","AlbertForSequenceClassification","The","restored","model"]
code: def pretrained(
      name="deberta_base_sequence_classifier_imdb",
      lang="en",
      remote_loc=None,
  ):
      """Downloads and loads a pretrained model.
  
      Parameters
      ----------
      name : str, optional
          Name of the pretrained model, by default
          "deberta_v3_xsmall_sequence_classifier_imdb"
      lang : str, optional
          Language of the pretrained model, by default "en"
      remote_loc : str, optional
          Optional remote address of the resource, by default None. Will use
          Spark NLPs repositories otherwise.
  
      Returns
      -------
      AlbertForSequenceClassification
          The restored model
      """
      from sparknlp.pretrained import ResourceDownloader
  
      return ResourceDownloader.downloadModel(
          DeBertaForSequenceClassification, name, lang, remote_loc
      )
  
file_path: spark-nlp/python/sparknlp/annotator/classifier_dl/deberta_for_sequence_classification.py
imports: from sparknlp.common import *
label_desc: Use image node_id 6 for calling the DeBertaForSequenceClassification obj's underlying member method code with example usage: obj.pretrained(name, lang, remote_loc) and returns: ResourceDownloader