SPARKNLP 836 - Introducing "Instructor Embeddings" for sentence embeddings like Instructor-XL model

python/sparknlp/annotator/embeddings/__init__.py

@@ -22,6 +22,7 @@
 from sparknlp.annotator.embeddings.distil_bert_embeddings import *
 from sparknlp.annotator.embeddings.doc2vec import *
 from sparknlp.annotator.embeddings.elmo_embeddings import *
+from sparknlp.annotator.embeddings.instructor_embeddings import *
 from sparknlp.annotator.embeddings.longformer_embeddings import *
 from sparknlp.annotator.embeddings.roberta_embeddings import *
 from sparknlp.annotator.embeddings.roberta_sentence_embeddings import *

python/sparknlp/annotator/embeddings/instructor_embeddings.py

@@ -0,0 +1,204 @@
+#  Copyright 2017-2022 John Snow Labs
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+"""Contains classes for BertEmbeddings."""
+
+from sparknlp.common import *
+
+
+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...|
+    +--------------------------------------------------------------------------------+
+    """
+
+    name = "InstructorEmbeddings"
+
+    inputAnnotatorTypes = [AnnotatorType.DOCUMENT]
+
+    outputAnnotatorType = AnnotatorType.SENTENCE_EMBEDDINGS
+    instruction = Param(Params._dummy(), "instruction", "Set transformer instruction, e.g. 'summarize:'",
+                        typeConverter=TypeConverters.toString)
+    configProtoBytes = Param(Params._dummy(),
+                             "configProtoBytes",
+                             "ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()",
+                             TypeConverters.toListInt)
+
+    def setInstruction(self, value):
+        """ Sets transformer instruction, e.g. 'summarize:'.
+
+        Parameters
+        ----------
+        value : str
+        """
+        return self._set(instruction=value)
+
+    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
+    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
+    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)

python/sparknlp/internal/__init__.py

@@ -529,3 +529,8 @@ def __init__(self, path, jspark):
         super(_RoBertaForZeroShotClassification, self).__init__(
             "com.johnsnowlabs.nlp.annotators.classifier.dl.RoBertaForZeroShotClassification.loadSavedModel", path,
             jspark)
+
+
+class _InstructorLoader(ExtendedJavaWrapper):
+    def __init__(self, path, jspark):
+        super(_InstructorLoader, self).__init__("com.johnsnowlabs.nlp.embeddings.InstructorEmbeddings.loadSavedModel", path, jspark)

python/test/annotator/embeddings/instructor_embeddings_test.py

@@ -0,0 +1,85 @@
+#  Copyright 2017-2022 John Snow Labs
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+import os
+import unittest
+
+import pytest
+
+from sparknlp.annotator import *
+from sparknlp.base import *
+from test.annotator.common.has_max_sentence_length_test import HasMaxSentenceLengthTests
+from test.util import SparkContextForTest
+
+
+@pytest.mark.slow
+class InstructorEmbeddingsTestSpec(unittest.TestCase):
+    def setUp(self):
+        self.spark = SparkContextForTest.spark
+        self.tested_annotator = InstructorEmbeddings.pretrained() \
+            .setInstruction("Represent the Wikipedia document for retrieval: ") \
+            .setInputCols(["documents"]) \
+            .setOutputCol("instructor")
+
+    def runTest(self):
+        data = self.spark.createDataFrame([
+            [1, """Capitalism has been dominant in the Western world since the end of feudalism, but most feel[who?] that 
+            the term "mixed economies" more precisely describes most contemporary economies, due to their containing both 
+            private-owned and state-owned enterprises. In capitalism, prices determine the demand-supply scale. For 
+            example, higher demand for certain goods and services lead to higher prices and lower demand for certain 
+            goods lead to lower prices. """]]).toDF("id", "text")
+
+        document_assembler = DocumentAssembler() \
+            .setInputCol("text") \
+            .setOutputCol("documents")
+
+        instruction = self.tested_annotator
+
+        pipeline = Pipeline().setStages([document_assembler, instruction])
+        results = pipeline.fit(data).transform(data)
+
+        results.select("instructor.embeddings").show(truncate=False)
+
+#
+# @pytest.mark.slow
+# class BertEmbeddingsLoadSavedModelTestSpec(unittest.TestCase):
+#
+#     def setUp(self):
+#         self.data = SparkContextForTest.spark.read.option("header", "true") \
+#             .csv(path="file:///" + os.getcwd() + "/../src/test/resources/embeddings/sentence_embeddings.csv")
+#
+#     def runTest(self):
+#         document_assembler = DocumentAssembler() \
+#             .setInputCol("text") \
+#             .setOutputCol("document")
+#         sentence_detector = SentenceDetector() \
+#             .setInputCols(["document"]) \
+#             .setOutputCol("sentence")
+#         tokenizer = Tokenizer() \
+#             .setInputCols(["sentence"]) \
+#             .setOutputCol("token")
+#         albert = BertEmbeddings.loadSavedModel(os.getcwd() + "/../src/test/resources/tf-hub-bert/model",
+#                                                SparkContextForTest.spark) \
+#             .setInputCols(["sentence", "token"]) \
+#             .setOutputCol("embeddings")
+#
+#         pipeline = Pipeline(stages=[
+#             document_assembler,
+#             sentence_detector,
+#             tokenizer,
+#             albert
+#         ])
+#
+#         model = pipeline.fit(self.data)
+#         model.write().overwrite().save("./tmp_bert_pipeline_model")
+#         model.transform(self.data).show()