class_docstr: The XLM-RoBERTa model was proposed in `Unsupervised Cross-lingual
  Representation Learning at Scale` by Alexis Conneau, Kartikay Khandelwal,
  Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzman, Edouard
  Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
  
  It is based on Facebook's RoBERTa model released in 2019. It is a large
  multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data.
  
  Pretrained models can be loaded with :meth:`.pretrained` of the companion
  object:
  
  >>> embeddings = XlmRoBertaEmbeddings.pretrained() \
  ...     .setInputCols(["document", "token"]) \
  ...     .setOutputCol("embeddings")
  
  The default model is ``"xlm_roberta_base"``, default language is ``"xx"``
  (meaning multi-lingual), if no values are 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-%20XLM-RoBERTa.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
  -----
  - XLM-RoBERTa is a multilingual model trained on 100 different languages.
    Unlike some XLM multilingual models, it does not require **lang**
    parameter to understand which language is used, and should be able to
    determine the correct language from the input ids.
  - This implementation is the same as RoBERTa. Refer to
    :class:`.RoBertaEmbeddings` for usage examples as well as the information
    relative to the inputs and outputs.
  
  References
  ----------
  `Unsupervised Cross-lingual
  Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`__
  
  **Paper Abstract:**
  
  *This paper shows that pretraining multilingual language models at scale
  leads to significant performance gains for a wide range of cross-lingual
  transfer tasks. We train a Transformer-based masked language model on one
  hundred languages, using more than two terabytes of filtered CommonCrawl
  data. Our model, dubbed XLM-R, significantly outperforms multilingual BERT
  (mBERT) on a variety of cross-lingual benchmarks, including +13.8% average
  accuracy on XNLI, +12.3% average F1 score on MLQA, and +2.1% average F1
  score on NER. XLM-R performs particularly well on low-resource languages,
  improving 11.8% in XNLI accuracy for Swahili and 9.2% for Urdu over the
  previous XLM model. We also present a detailed empirical evaluation of the
  key factors that are required to achieve these gains, including the
  trade-offs between (1) positive transfer and capacity dilution and (2) the
  performance of high and low resource languages at scale. Finally, we show,
  for the first time, the possibility of multilingual modeling without
  sacrificing per-language performance; XLM-Ris very competitive with strong
  monolingual models on the GLUE and XNLI benchmarks. We will make XLM-R code,
  data, and models publicly available.*
  
  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 = XlmRoBertaEmbeddings.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|
  +--------------------------------------------------------------------------------+
  |[-0.05969233065843582,-0.030789051204919815,0.04443822056055069,0.09564960747...|
  |[-0.038839809596538544,0.011712731793522835,0.019954433664679527,0.0667808502...|
  |[-0.03952755779027939,-0.03455188870429993,0.019103847444057465,0.04311436787...|
  |[-0.09579929709434509,0.02494969218969345,-0.014753809198737144,0.10259044915...|
  |[0.004710011184215546,-0.022148698568344116,0.011723337695002556,-0.013356896...|
  +--------------------------------------------------------------------------------+
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docstr: Loads a locally saved model.
  
  Parameters
  ----------
  folder : str
      Folder of the saved model
  spark_session : pyspark.sql.SparkSession
      The current SparkSession
  
  Returns
  -------
  XlmRoBertaEmbeddings
      The restored model
docstr_tok: ["Loads","a","locally","saved","model",".","Parameters","--","--","--","--","--","folder",":","str","Folder","of","the","saved","model","spark_session",":","pyspark.sql.SparkSession","The","current","SparkSession","Returns","--","--","--","-","XlmRoBertaEmbeddings","The","restored","model"]
code: def loadSavedModel(folder, spark_session):
      """Loads a locally saved model.
  
      Parameters
      ----------
      folder : str
          Folder of the saved model
      spark_session : pyspark.sql.SparkSession
          The current SparkSession
  
      Returns
      -------
      XlmRoBertaEmbeddings
          The restored model
      """
      from sparknlp.internal import _XlmRoBertaLoader
  
      jModel = _XlmRoBertaLoader(
          folder, spark_session._jsparkSession
      )._java_obj
      return XlmRoBertaEmbeddings(java_model=jModel)
  
file_path: spark-nlp/python/sparknlp/annotator/embeddings/xlm_roberta_embeddings.py
imports: from sparknlp.common import *
label_desc: Use image node_id 3 for calling the XlmRoBertaEmbeddings obj's underlying member method code with example usage: obj.loadSavedModel(folder, spark_session) and returns: XlmRoBertaEmbeddings