class_docstr: Creates word 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 :meth:`.pretrained` of the companion
  object:
  
  >>> embeddings = RoBertaEmbeddings.pretrained() \
  ...     .setInputCols(["document", "token"]) \
  ...     .setOutputCol("embeddings")
  
  The default model is ``"roberta_base"``, if no name is provided. For
  available pretrained models please see the `Models Hub
  <https://sparknlp.org/models?task=Embeddings>`__.
  
  For extended examples of usage, see the `Examples
  <https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20RoBERTa.ipynb>`__.
  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``    ``WORD_EMBEDDINGS``
  ====================== ======================
  
  Parameters
  ----------
  batchSize
      Size of every batch, by default 8
  dimension
      Number of embedding dimensions, by default 768
  caseSensitive
      Whether to ignore case in tokens for embeddings matching, by default
      True
  maxSentenceLength
      Max sentence length to process, by default 128
  configProtoBytes
      ConfigProto from tensorflow, serialized into byte array.
  
  Notes
  -----
  - RoBERTa has the same architecture as BERT, but uses a byte-level BPE as
    a tokenizer (same as GPT-2) and uses a different pretraining scheme.
  - RoBERTa doesn't have ``token_type_ids``, you don't need to indicate
    which token belongs to which segment. Just separate your segments with
    the separation token ``tokenizer.sep_token`` (or ``</s>``)
  
  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.*
  
  Source of the original code: `RoBERTa: A Robustly Optimized BERT Pretraining
  Approach on GitHub
  <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__.
  
  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")
  >>> embeddings = RoBertaEmbeddings.pretrained() \
  ...     .setInputCols(["document", "token"]) \
  ...     .setOutputCol("embeddings") \
  ...     .setCaseSensitive(True)
  >>> embeddingsFinisher = EmbeddingsFinisher() \
  ...     .setInputCols(["embeddings"]) \
  ...     .setOutputCols("finished_embeddings") \
  ...     .setOutputAsVector(True) \
  ...     .setCleanAnnotations(False)
  >>> pipeline = Pipeline() \
  ...     .setStages([
  ...       documentAssembler,
  ...       tokenizer,
  ...       embeddings,
  ...       embeddingsFinisher
  ...     ])
  >>> data = spark.createDataFrame([["This is a sentence."]]).toDF("text")
  >>> result = pipeline.fit(data).transform(data)
  >>> result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
  +--------------------------------------------------------------------------------+
  |                                                                          result|
  +--------------------------------------------------------------------------------+
  |[0.18792399764060974,-0.14591649174690247,0.20547787845134735,0.1468472778797...|
  |[0.22845706343650818,0.18073144555091858,0.09725798666477203,-0.0417917296290...|
  |[0.07037967443466187,-0.14801117777824402,-0.03603338822722435,-0.17893412709...|
  |[-0.08734266459941864,0.2486150562763214,-0.009067727252840996,-0.24408400058...|
  |[0.22409197688102722,-0.4312366545200348,0.1401449590921402,0.356410235166549...|
  +--------------------------------------------------------------------------------+
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docstr: Sets configProto from tensorflow, serialized into byte array.
  
  Parameters
  ----------
  b : List[int]
      ConfigProto from tensorflow, serialized into byte array
docstr_tok: ["Sets","configProto","from","tensorflow",",","serialized","into","byte","array",".","Parameters","--","--","--","--","--","b",":","List","[","int","]","ConfigProto","from","tensorflow",",","serialized","into","byte","array"]
code: def setConfigProtoBytes(self, b):
      """Sets configProto from tensorflow, serialized into byte array.
  
      Parameters
      ----------
      b : List[int]
          ConfigProto from tensorflow, serialized into byte array
      """
      return self._set(configProtoBytes=b)
  
file_path: spark-nlp/python/sparknlp/annotator/embeddings/roberta_embeddings.py
imports: from sparknlp.common import *
label_desc: Use image node_id 1 for calling the RoBertaEmbeddings obj's underlying member method code with example usage: obj.setConfigProtoBytes(b) and returns: self