# Copyright 2017-2022 John Snow Labs
#
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# http://www.apache.org/licenses/LICENSE-2.0
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"""Contains classes for BertEmbeddings."""
from sparknlp.common import *
[docs]class InstructorEmbeddings(AnnotatorModel,
HasEmbeddingsProperties,
HasCaseSensitiveProperties,
HasStorageRef,
HasBatchedAnnotate,
HasMaxSentenceLengthLimit):
"""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...|
+--------------------------------------------------------------------------------+
"""
[docs] name = "InstructorEmbeddings"
[docs] outputAnnotatorType = AnnotatorType.SENTENCE_EMBEDDINGS
[docs] instruction = Param(Params._dummy(), "instruction", "Set transformer instruction, e.g. 'summarize:'",
typeConverter=TypeConverters.toString)
[docs] configProtoBytes = Param(Params._dummy(),
"configProtoBytes",
"ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()",
TypeConverters.toListInt)
[docs] def setInstruction(self, value):
""" Sets transformer instruction, e.g. 'summarize:'.
Parameters
----------
value : str
"""
return self._set(instruction=value)
[docs] 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)
@keyword_only
def __init__(self, classname="com.johnsnowlabs.nlp.embeddings.InstructorEmbeddings", java_model=None):
super(InstructorEmbeddings, self).__init__(
classname=classname,
java_model=java_model
)
self._setDefault(
dimension=768,
batchSize=8,
maxSentenceLength=128,
caseSensitive=False,
instruction="",
)
@staticmethod
[docs] 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)
@staticmethod
[docs] def pretrained(name="instructor_base", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default "instructor_base"
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
-------
InstructorEmbeddings
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(InstructorEmbeddings, name, lang, remote_loc)