class_docstr: Sentence-level embeddings using XLM-RoBERTa. 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 GuzmÃ¡n, 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 pretrained of the companion object:
  
  Pretrained models can be loaded with :meth:`.pretrained` of the companion
  object:
  
  >>> embeddings = XlmRoBertaSentenceEmbeddings.pretrained() \
  ...    .setInputCols(["sentence"]) \
  ...    .setOutputCol("sentence_embeddings")
  
  
  The default model is ``"sent_xlm_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
  ----------
  `Unsupervised Cross-lingual Representation Learning at Scale <https://arxiv.org/pdf/1911.02116.pdf>`__
  
  **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")
  >>> sentence = SentenceDetector() \
  ...     .setInputCols(["document"]) \
  ...     .setOutputCol("sentence")
  >>> embeddings = XlmRoBertaSentenceEmbeddings.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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code: def __init__(
      self,
      classname="com.johnsnowlabs.nlp.embeddings.XlmRoBertaSentenceEmbeddings",
      java_model=None,
  ):
      super(XlmRoBertaSentenceEmbeddings, self).__init__(
          classname=classname, java_model=java_model
      )
      self._setDefault(
          dimension=768,
          batchSize=8,
          maxSentenceLength=128,
          caseSensitive=True,
      )
  
file_path: spark-nlp/python/sparknlp/annotator/embeddings/xlm_roberta_sentence_embeddings.py
imports: from sparknlp.common import *
label_desc: Use image node_id 2 to create a new XlmRoBertaSentenceEmbeddings object from inherited base classes: AnnotatorModel, HasEmbeddingsProperties, HasCaseSensitiveProperties, HasStorageRef, HasBatchedAnnotate, HasEngine, HasMaxSentenceLengthLimit with example: obj = XlmRoBertaSentenceEmbeddings(classname, java_model)