class_docstr: ALBERT: A Lite Bert For Self-Supervised Learning Of Language
  Representations - Google Research, Toyota Technological Institute at Chicago
  
  These word embeddings represent the outputs generated by the Albert model.
  All official Albert releases by google in TF-HUB are supported with this
  Albert Wrapper:
  
  **Ported TF-Hub Models:**
  
  ============================ ============================================================== =====================================================
  Model Name                   TF-Hub Model                                                   Model Properties
  ============================ ============================================================== =====================================================
  ``"albert_base_uncased"``    `albert_base <https://tfhub.dev/google/albert_base/3>`__       768-embed-dim,   12-layer,  12-heads, 12M parameters
  ``"albert_large_uncased"``   `albert_large <https://tfhub.dev/google/albert_large/3>`__     1024-embed-dim,  24-layer,  16-heads, 18M parameters
  ``"albert_xlarge_uncased"``  `albert_xlarge <https://tfhub.dev/google/albert_xlarge/3>`__   2048-embed-dim,  24-layer,  32-heads, 60M parameters
  ``"albert_xxlarge_uncased"`` `albert_xxlarge <https://tfhub.dev/google/albert_xxlarge/3>`__ 4096-embed-dim,  12-layer,  64-heads, 235M parameters
  ============================ ============================================================== =====================================================
  
  This model requires input tokenization with SentencePiece model, which is
  provided by Spark-NLP (See tokenizers package).
  
  Pretrained models can be loaded with :meth:`.pretrained` of the companion
  object:
  
  >>> embeddings = AlbertEmbeddings.pretrained() \
  ...    .setInputCols(["sentence", "token"]) \
  ...    .setOutputCol("embeddings")
  
  
  The default model is ``"albert_base_uncased"``, if no name is provided.
  
  For extended examples of usage, see the `Examples
  <https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/dl-ner/ner_albert.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
      False
  configProtoBytes
      ConfigProto from tensorflow, serialized into byte array.
  maxSentenceLength
      Max sentence length to process, by default 128
  
  Notes
  -----
  ALBERT uses repeating layers which results in a small memory footprint,
  however the computational cost remains similar to a BERT-like architecture
  with the same number of hidden layers as it has to iterate through the same
  number of (repeating) layers.
  
  References
  ----------
  `ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS <https://arxiv.org/pdf/1909.11942.pdf>`__
  
  https://github.com/google-research/ALBERT
  
  https://tfhub.dev/s?q=albert
  
  **Paper abstract:**
  
  *Increasing model size when pretraining natural language representations
  often results in improved performance on downstream tasks. However, at some
  point further model increases become harder due to GPU/TPU memory
  limitations and longer training times. To address these problems, we present
  two parameter reduction techniques to lower memory consumption and increase
  the training speed of BERT (Devlin et al., 2019). Comprehensive empirical
  evidence shows that our proposed methods lead to models that scale much
  better compared to the original BERT. We also use a self-supervised loss
  that focuses on modeling inter-sentence coherence, and show it consistently
  helps downstream tasks with multi-sentence inputs. As a result, our best
  model establishes new state-of-the-art results on the GLUE, RACE, and SQuAD
  benchmarks while having fewer parameters compared to BERT-large.*
  
  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"]) \
  >>> embeddings = AlbertEmbeddings.pretrained() \
  ...     .setInputCols(["token", "document"]) \
  ...     .setOutputCol("embeddings")
  >>> 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|
  +--------------------------------------------------------------------------------+
  |[1.1342473030090332,-1.3855540752410889,0.9818322062492371,-0.784737348556518...|
  |[0.847029983997345,-1.047153353691101,-0.1520637571811676,-0.6245765686035156...|
  |[-0.009860038757324219,-0.13450059294700623,2.707749128341675,1.2916892766952...|
  |[-0.04192575812339783,-0.5764210224151611,-0.3196685314178467,-0.527840495109...|
  |[0.15583214163780212,-0.1614152491092682,-0.28423872590065,-0.135491415858268...|
  +--------------------------------------------------------------------------------+
  
  See Also
  --------
  AlbertForTokenClassification : for  AlbertEmbeddings with a token classification layer on top
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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/albert_embeddings.py
imports: from sparknlp.common import *
label_desc: Use image node_id 1 for calling the AlbertEmbeddings obj's underlying member method code with example usage: obj.setConfigProtoBytes(b) and returns: self