class_docstr: Sentence embeddings using INSTRUCTOR.
  
  Instructor👨‍🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) by simply providing the task instruction, without any finetuning. Instructor👨‍ achieves sota on 70 diverse embedding tasks!
  
  Pretrained models can be loaded with :meth:`.pretrained` of the companion
  object:
  
  >>> embeddings = InstructorEmbeddings.pretrained() \
  ...     .setInputCols(["document"]) \
  ...     .setInstruction("Represent the Medicine sentence for clustering: ") \
  ...     .setOutputCol("instructor_embeddings")
  
  
  The default model is ``"instructor_base"``, if no name is provided.
  
  For available pretrained models please see the
  `Models Hub <https://sparknlp.org/models?q=Instructor>`__.
  
  
  ====================== ======================
  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
  instruction
      Set transformer instruction, e.g. 'summarize:'
  maxSentenceLength
      Max sentence length to process, by default 128
  configProtoBytes
      ConfigProto from tensorflow, serialized into byte array.
  
  References
  ----------
  `One Embedder, Any Task: Instruction-Finetuned Text Embeddings <https://arxiv.org/abs/2212.09741>`__
  
  https://github.com/HKUNLP/instructor-embedding/
  
  **Paper abstract**
  
  *We introduce INSTRUCTOR, a new method for computing text embeddings given task instructions:
  every text input is embedded together with instructions explaining the use case (e.g., task and
  domain descriptions). Unlike encoders from prior work that are more specialized, INSTRUCTOR is a
  single embedder that can generate text embeddings tailored to different downstream tasks and domains,
  without any further training. We first annotate instructions for 330 diverse tasks and train INSTRUCTOR
  on this multitask mixture with a contrastive loss. We evaluate INSTRUCTOR on 70 embedding evaluation tasks
  (66 of which are unseen during training), ranging from classification and information retrieval to semantic
  textual similarity and text generation evaluation. INSTRUCTOR, while having an order of magnitude fewer
  parameters than the previous best model, achieves state-of-the-art performance, with an average improvement
  of 3.4% compared to the previous best results on the 70 diverse datasets. Our analysis suggests that
  INSTRUCTOR is robust to changes in instructions, and that instruction finetuning mitigates the challenge of
  training a single model on diverse datasets. Our model, code, and data are available at this https
  URL <https://instructor-embedding.github.io/>.*
  
  Examples
  --------
  >>> import sparknlp
  >>> from sparknlp.base import *
  >>> from sparknlp.annotator import *
  >>> from pyspark.ml import Pipeline
  >>> documentAssembler = DocumentAssembler() \
  ...     .setInputCol("text") \
  ...     .setOutputCol("document")
  >>> embeddings = InstructorEmbeddings.pretrained() \
  ...     .setInputCols(["document"]) \
  ...     .setInstruction("Represent the Medicine sentence for clustering: ") \
  ...     .setOutputCol("instructor_embeddings")
  >>> embeddingsFinisher = EmbeddingsFinisher() \
  ...     .setInputCols(["instructor_embeddings"]) \
  ...     .setOutputCols("finished_embeddings") \
  ...     .setOutputAsVector(True)
  >>> pipeline = Pipeline().setStages([
  ...     documentAssembler,
  ...     embeddings,
  ...     embeddingsFinisher
  ... ])
  >>> data = spark.createDataFrame([["Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity"]]).toDF("text")
  >>> result = pipeline.fit(data).transform(data)
  >>> result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
  +--------------------------------------------------------------------------------+
  |                                                                          result|
  +--------------------------------------------------------------------------------+
  |[-2.3497989177703857,0.480538547039032,-0.3238905668258667,-1.612930893898010...|
  +--------------------------------------------------------------------------------+
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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
  -------
  InstructorEmbeddings
      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","--","--","--","-","InstructorEmbeddings","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
      -------
      InstructorEmbeddings
          The restored model
      """
      from sparknlp.internal import _InstructorLoader
  
      jModel = _InstructorLoader(
          folder, spark_session._jsparkSession
      )._java_obj
      return InstructorEmbeddings(java_model=jModel)
  
file_path: spark-nlp/python/sparknlp/annotator/embeddings/instructor_embeddings.py
imports: from sparknlp.common import *
label_desc: Use image node_id 4 for calling the InstructorEmbeddings obj's underlying member method code with example usage: obj.loadSavedModel(folder, spark_session) and returns: InstructorEmbeddings