class_docstr: Sentence-level embeddings using BERT. BERT (Bidirectional Encoder
  Representations from Transformers) provides dense vector representations for
  natural language by using a deep, pre-trained neural network with the
  Transformer architecture.
  
  Pretrained models can be loaded with :meth:`.pretrained` of the companion
  object:
  
  >>>embeddings = BertSentenceEmbeddings.pretrained() \
  ...    .setInputCols(["sentence"]) \
  ...    .setOutputCol("sentence_bert_embeddings")
  
  
  The default model is ``"sent_small_bert_L2_768"``, 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-%20BERT%20Sentence.ipynb>`__.
  
  ====================== =======================
  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
  isLong
      Use Long type instead of Int type for inputs buffer - Some Bert models
      require Long instead of Int.
  configProtoBytes
      ConfigProto from tensorflow, serialized into byte array.
  
  References
  ----------
  `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`__
  
  https://github.com/google-research/bert
  
  **Paper abstract**
  
  *We introduce a new language representation model called BERT, which stands
  for Bidirectional Encoder Representations from Transformers. Unlike recent
  language representation models, BERT is designed to pre-train deep
  bidirectional representations from unlabeled text by jointly conditioning on
  both left and right context in all layers. As a result, the pre-trained BERT
  model can be fine-tuned with just one additional output layer to create
  state-of-the-art models for a wide range of tasks, such as question
  answering and language inference, without substantial task-specific
  architecture modifications. BERT is conceptually simple and empirically
  powerful. It obtains new state-of-the-art results on eleven natural language
  processing tasks, including pushing the GLUE score to 80.5% (7.7% point
  absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute
  improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point
  absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute
  improvement).*
  
  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 = BertSentenceEmbeddings.pretrained("sent_small_bert_L2_128") \
  ...     .setInputCols(["sentence"]) \
  ...     .setOutputCol("sentence_bert_embeddings")
  >>> embeddingsFinisher = EmbeddingsFinisher() \
  ...     .setInputCols(["sentence_bert_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,...|
  +--------------------------------------------------------------------------------+
docstr: Downloads and loads a pretrained model.
  
  Parameters
  ----------
  name : str, optional
      Name of the pretrained model, by default "sent_small_bert_L2_768"
  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
  -------
  BertSentenceEmbeddings
      The restored model
docstr_tok: ["Downloads","and","loads","a","pretrained","model",".","Parameters","--","--","--","--","--","name",":","str",",","optional","Name","of","the","pretrained","model",",","by","default","``","sent_small_bert_L2_768","''","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","--","--","--","-","BertSentenceEmbeddings","The","restored","model"]
code: def pretrained(
      name="sent_small_bert_L2_768", lang="en", remote_loc=None
  ):
      """Downloads and loads a pretrained model.
  
      Parameters
      ----------
      name : str, optional
          Name of the pretrained model, by default "sent_small_bert_L2_768"
      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
      -------
      BertSentenceEmbeddings
          The restored model
      """
      from sparknlp.pretrained import ResourceDownloader
  
      return ResourceDownloader.downloadModel(
          BertSentenceEmbeddings, name, lang, remote_loc
      )
  
file_path: spark-nlp/python/sparknlp/annotator/embeddings/bert_sentence_embeddings.py
imports: from sparknlp.common import *
label_desc: Use image node_id 5 for calling the BertSentenceEmbeddings obj's underlying member method code with example usage: obj.pretrained(name, lang, remote_loc) and returns: ResourceDownloader