class_docstr: Sentence embeddings using BGE.
  
   BGE, or BAAI General Embeddings, a model that can map any text to a low-dimensional dense 
  vector which can be used for tasks like retrieval, classification, clustering, or semantic search.
  
  Pretrained models can be loaded with `pretrained` of the companion object:
  
    >>> embeddings = BGEEmbeddings.pretrained() \
    ...     .setInputCols(["document"]) \
    ...     .setOutputCol("bge_embeddings")
  
  
    The default model is ``"bge_base"``, if no name is provided.
  
    For available pretrained models please see the
    `Models Hub <https://sparknlp.org/models?q=BGE>`__.
  
  
    ====================== ======================
    Input Annotation types Output Annotation type
    ====================== ======================
    ``DOCUMENT``            ``SENTENCE_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 False
    maxSentenceLength
        Max sentence length to process, by default 512
    configProtoBytes
        ConfigProto from tensorflow, serialized into byte array.
  
    References
    ----------
    `C-Pack: Packaged Resources To Advance General Chinese Embedding <https://arxiv.org/pdf/2309.07597>`__
    `BGE Github Repository <https://github.com/FlagOpen/FlagEmbedding>`__
  
    **Paper abstract**
  
    *We introduce C-Pack, a package of resources that significantly advance the field of general
    Chinese embeddings. C-Pack includes three critical resources. 
    1) C-MTEB is a comprehensive benchmark for Chinese text embeddings covering 6 tasks and 35 datasets.
    2) C-MTP is a massive text embedding dataset curated from labeled and unlabeled Chinese corpora
    for training embedding models.
    3) C-TEM is a family of embedding models covering multiple sizes.
    Our models outperform all prior Chinese text embeddings on C-MTEB by up to +10% upon the 
    time of the release. We also integrate and optimize the entire suite of training methods for
    C-TEM. Along with our resources on general Chinese embedding, we release our data and models for
    English text embeddings. The English models achieve stateof-the-art performance on the MTEB
    benchmark; meanwhile, our released English data is 2 times larger than the Chinese data. All
    these resources are made publicly available at https://github.com/FlagOpen/FlagEmbedding.*
  
    Examples
    --------
    >>> import sparknlp
    >>> from sparknlp.base import *
    >>> from sparknlp.annotator import *
    >>> from pyspark.ml import Pipeline
    >>> documentAssembler = DocumentAssembler() \
    ...     .setInputCol("text") \
    ...     .setOutputCol("document")
    >>> embeddings = BGEEmbeddings.pretrained() \
    ...     .setInputCols(["document"]) \
    ...     .setOutputCol("bge_embeddings")
    >>> embeddingsFinisher = EmbeddingsFinisher() \
    ...     .setInputCols(["bge_embeddings"]) \
    ...     .setOutputCols("finished_embeddings") \
    ...     .setOutputAsVector(True)
    >>> pipeline = Pipeline().setStages([
    ...     documentAssembler,
    ...     embeddings,
    ...     embeddingsFinisher
    ... ])
    >>> data = spark.createDataFrame([["query: how much protein should a female eat",
    ... "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day." +     ... "But, as you can see from this chart, you'll need to increase that if you're expecting or training for a" +     ... "marathon. Check out the chart below to see how much protein you should be eating each day.",
    ... ]]).toDF("text")
    >>> result = pipeline.fit(data).transform(data)
    >>> result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
    +--------------------------------------------------------------------------------+
    |                                                                          result|
    +--------------------------------------------------------------------------------+
    |[[8.0190285E-4, -0.005974853, -0.072875895, 0.007944068, 0.026059335, -0.0080...|
    |[[0.050514214, 0.010061974, -0.04340176, -0.020937217, 0.05170225, 0.01157857...|
    +--------------------------------------------------------------------------------+
    
docstr: 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/bge_embeddings.py
imports: from sparknlp.common import *
label_desc: Use image node_id 1 for calling the BGEEmbeddings obj's underlying member method code with example usage: obj.setConfigProtoBytes(b) and returns: self