class_docstr: Sentence-level embeddings using RoBERTa. The RoBERTa model was proposed in RoBERTa: A Robustly Optimized BERT
  Pretraining Approach  by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy,
  Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. It is based on Google's BERT model released in 2018. It builds on
  BERT and modifies key hyperparameters, removing the next-sentence pretraining objective and training with much
  larger mini-batches and learning rates. Pretrained models can be loaded with pretrained of the companion object:
  
  Pretrained models can be loaded with :meth:`.pretrained` of the companion
  object:
  
  >>> embeddings = RoBertaSentenceEmbeddings.pretrained() \
  ...    .setInputCols(["sentence"]) \
  ...    .setOutputCol("sentence_embeddings")
  
  
  The default model is ``"sent_roberta_base"``, if no name is provided.
  
  For available pretrained models please see the
  `Models Hub <https://sparknlp.org/models?task=Embeddings>`__.
  
  ====================== =======================
  Input Annotation types Output Annotation type
  ====================== =======================
  ``DOCUMENT``           ``SENTENCE_EMBEDDINGS``
  ====================== =======================
  
  Parameters
  ----------
  batchSize
      Size of every batch, by default 8
  caseSensitive
      Whether to ignore case in tokens for embeddings matching, by default
      False
  dimension
      Number of embedding dimensions, by default 768
  maxSentenceLength
      Max sentence length to process, by default 128
  configProtoBytes
      ConfigProto from tensorflow, serialized into byte array.
  
  References
  ----------
  `RoBERTa: A Robustly Optimized BERT Pretraining Approach <https://arxiv.org/abs/1907.11692>`__
  
  **Paper abstract:**
  
  *Language model pretraining has led to significant performance gains but careful comparison between different
  approaches is challenging. Training is computationally expensive, often done on private datasets of different
  sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a
  replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key
  hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed
  the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE,
  RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise
  questions about the source of recently reported improvements. We release our models and code.*
  
  Examples
  --------
  >>> import sparknlp
  >>> from sparknlp.base import *
  >>> from sparknlp.annotator import *
  >>> from pyspark.ml import Pipeline
  >>> documentAssembler = DocumentAssembler() \
  ...     .setInputCol("text") \
  ...     .setOutputCol("document")
  >>> sentence = SentenceDetector() \
  ...     .setInputCols(["document"]) \
  ...     .setOutputCol("sentence")
  >>> embeddings = RoBertaSentenceEmbeddings.pretrained() \
  ...     .setInputCols(["sentence"]) \
  ...     .setOutputCol("sentence_embeddings")
  >>> embeddingsFinisher = EmbeddingsFinisher() \
  ...     .setInputCols(["sentence_embeddings"]) \
  ...     .setOutputCols("finished_embeddings") \
  ...     .setOutputAsVector(True)
  >>> pipeline = Pipeline().setStages([
  ...     documentAssembler,
  ...     sentence,
  ...     embeddings,
  ...     embeddingsFinisher
  ... ])
  >>> data = spark.createDataFrame([["John loves apples. Mary loves oranges. John loves Mary."]]).toDF("text")
  >>> result = pipeline.fit(data).transform(data)
  >>> result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
  +--------------------------------------------------------------------------------+
  |                                                                          result|
  +--------------------------------------------------------------------------------+
  |[-0.8951074481010437,0.13753940165042877,0.3108254075050354,-1.65693199634552...|
  |[-0.6180210709571838,-0.12179657071828842,-0.191165953874588,-1.4497021436691...|
  |[-0.822715163230896,0.7568016648292542,-0.1165061742067337,-1.59048593044281,...|
  +--------------------------------------------------------------------------------+
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docstr: Downloads and loads a pretrained model.
  
  Parameters
  ----------
  name : str, optional
      Name of the pretrained model, by default "sent_roberta_base"
  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
  -------
  RoBertaSentenceEmbeddings
      The restored model
docstr_tok: ["Downloads","and","loads","a","pretrained","model",".","Parameters","--","--","--","--","--","name",":","str",",","optional","Name","of","the","pretrained","model",",","by","default","``","sent_roberta_base","''","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","--","--","--","-","RoBertaSentenceEmbeddings","The","restored","model"]
code: def pretrained(name="sent_roberta_base", lang="en", remote_loc=None):
      """Downloads and loads a pretrained model.
  
      Parameters
      ----------
      name : str, optional
          Name of the pretrained model, by default "sent_roberta_base"
      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
      -------
      RoBertaSentenceEmbeddings
          The restored model
      """
      from sparknlp.pretrained import ResourceDownloader
  
      return ResourceDownloader.downloadModel(
          RoBertaSentenceEmbeddings, name, lang, remote_loc
      )
  
file_path: spark-nlp/python/sparknlp/annotator/embeddings/roberta_sentence_embeddings.py
imports: from sparknlp.common import *
label_desc: Use image node_id 4 for calling the RoBertaSentenceEmbeddings obj's underlying member method code with example usage: obj.pretrained(name, lang, remote_loc) and returns: ResourceDownloader