implementing SnowFlake (#14353)

* implementing SnowFlake

* typo fix

---------

Co-authored-by: Maziyar Panahi <maziyar.panahi@iscpif.fr>

python/sparknlp/annotator/embeddings/__init__.py

@@ -37,4 +37,5 @@
 from sparknlp.annotator.embeddings.xlnet_embeddings import *
 from sparknlp.annotator.embeddings.bge_embeddings import *
 from sparknlp.annotator.embeddings.uae_embeddings import *
+from sparknlp.annotator.embeddings.snowflake_embeddings import *
 from sparknlp.annotator.embeddings.nomic_embeddings import *

python/sparknlp/annotator/embeddings/snowflake_embeddings.py

@@ -0,0 +1,202 @@
+#  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 SnowFlakeEmbeddings."""
+
+from sparknlp.common import *
+
+
+class SnowFlakeEmbeddings(AnnotatorModel,
+					HasEmbeddingsProperties,
+					HasCaseSensitiveProperties,
+					HasStorageRef,
+					HasBatchedAnnotate,
+					HasMaxSentenceLengthLimit):
+	"""Sentence embeddings using SnowFlake.
+
+    snowflake-arctic-embed is a suite of text embedding models that focuses on creating
+    high-quality retrieval models optimized for performance.
+
+	Pretrained models can be loaded with :meth:`.pretrained` of the companion
+	object:
+
+	>>> embeddings = SnowFlakeEmbeddings.pretrained() \\
+	...     .setInputCols(["document"]) \\
+	...     .setOutputCol("SnowFlake_embeddings")
+
+
+	The default model is ``"snowflake_artic_m"``, if no name is provided.
+
+	For available pretrained models please see the
+	`Models Hub <https://sparknlp.org/models?q=SnowFlake>`__.
+
+
+	====================== ======================
+	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
+	----------
+
+	`Arctic-Embed: Scalable, Efficient, and Accurate Text Embedding Models <https://arxiv.org/abs/2405.05374>`__
+	`Snowflake Arctic-Embed Models <https://github.com/Snowflake-Labs/arctic-embed>`__
+
+	**Paper abstract**
+
+	*The models are trained by leveraging existing open-source text representation models, such
+     as bert-base-uncased, and are trained in a multi-stage pipeline to optimize their retrieval
+     performance. First, the models are trained with large batches of query-document pairs where
+     negatives are derived in-batch—pretraining leverages about 400m samples of a mix of public
+     datasets and proprietary web search data. Following pretraining models are further optimized
+     with long training on a smaller dataset (about 1m samples) of triplets of query, positive
+     document, and negative document derived from hard harmful mining. Mining of the negatives and
+     data curation is crucial to retrieval accuracy. A detailed technical report will be available
+     shortly. *
+
+	Examples
+	--------
+	>>> import sparknlp
+	>>> from sparknlp.base import *
+	>>> from sparknlp.annotator import *
+	>>> from pyspark.ml import Pipeline
+	>>> documentAssembler = DocumentAssembler() \\
+	...     .setInputCol("text") \\
+	...     .setOutputCol("document")
+	>>> embeddings = SnowFlakeEmbeddings.pretrained() \\
+	...     .setInputCols(["document"]) \\
+	...     .setOutputCol("embeddings")
+	>>> embeddingsFinisher = EmbeddingsFinisher() \\
+	...     .setInputCols("embeddings") \\
+	...     .setOutputCols("finished_embeddings") \\
+	...     .setOutputAsVector(True)
+	>>> pipeline = Pipeline().setStages([
+	...     documentAssembler,
+	...     embeddings,
+	...     embeddingsFinisher
+	... ])
+	>>> data = spark.createDataFrame([["hello world", "hello moon"]]).toDF("text")
+	>>> result = pipeline.fit(data).transform(data)
+	>>> result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
+	+--------------------------------------------------------------------------------+
+	|                                                                          result|
+	+--------------------------------------------------------------------------------+
+	|[0.50387806, 0.5861606, 0.35129607, -0.76046336, -0.32446072, -0.117674336, 0...|
+	|[0.6660665, 0.961762, 0.24854276, -0.1018044, -0.6569202, 0.027635604, 0.1915...|
+	+--------------------------------------------------------------------------------+
+	"""
+
+	name = "SnowFlakeEmbeddings"
+
+	inputAnnotatorTypes = [AnnotatorType.DOCUMENT]
+
+	outputAnnotatorType = AnnotatorType.SENTENCE_EMBEDDINGS
+	poolingStrategy = Param(Params._dummy(),
+							"poolingStrategy",
+							"Pooling strategy to use for sentence embeddings",
+							TypeConverters.toString)
+
+	def setPoolingStrategy(self, value):
+		"""Pooling strategy to use for sentence embeddings.
+
+		Available pooling strategies for sentence embeddings are:
+		  - `"cls"`: leading `[CLS]` token
+		  - `"cls_avg"`: leading `[CLS]` token + mean of all other tokens
+		  - `"last"`: embeddings of the last token in the sequence
+		  - `"avg"`: mean of all tokens
+		  - `"max"`: max of all embedding features of the entire token sequence
+		  - `"int"`: An integer number, which represents the index of the token to use as the
+			embedding
+
+		Parameters
+		----------
+		value : str
+			Pooling strategy to use for sentence embeddings
+		"""
+
+		valid_strategies = {"cls", "cls_avg", "last", "avg", "max"}
+		if value in valid_strategies or value.isdigit():
+			return self._set(poolingStrategy=value)
+		else:
+			raise ValueError(f"Invalid pooling strategy: {value}. "
+							 f"Valid strategies are: {', '.join(self.valid_strategies)} or an integer.")
+
+	@keyword_only
+	def __init__(self, classname="com.johnsnowlabs.nlp.embeddings.SnowFlakeEmbeddings", java_model=None):
+		super(SnowFlakeEmbeddings, self).__init__(
+			classname=classname,
+			java_model=java_model
+		)
+		self._setDefault(
+			dimension=1024,
+			batchSize=8,
+			maxSentenceLength=512,
+			caseSensitive=False,
+			poolingStrategy="cls"
+		)
+
+	@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
+		-------
+		SnowFlakeEmbeddings
+			The restored model
+		"""
+		from sparknlp.internal import _SnowFlakeEmbeddingsLoader
+		jModel = _SnowFlakeEmbeddingsLoader(folder, spark_session._jsparkSession)._java_obj
+		return SnowFlakeEmbeddings(java_model=jModel)
+
+	@staticmethod
+	def pretrained(name="snowflake_artic_m", lang="en", remote_loc=None):
+		"""Downloads and loads a pretrained model.
+
+		Parameters
+		----------
+		name : str, optional
+			Name of the pretrained model, by default "snowflake_artic_m"
+		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
+		-------
+		SnowFlakeEmbeddings
+			The restored model
+		"""
+		from sparknlp.pretrained import ResourceDownloader
+		return ResourceDownloader.downloadModel(SnowFlakeEmbeddings, name, lang, remote_loc)

python/sparknlp/internal/__init__.py

@@ -938,3 +938,10 @@ def __init__(self, path, jspark):
         super(_UAEEmbeddingsLoader, self).__init__(
             "com.johnsnowlabs.nlp.embeddings.UAEEmbeddings.loadSavedModel", path, jspark
         )
+
+
+class _SnowFlakeEmbeddingsLoader(ExtendedJavaWrapper):
+    def __init__(self, path, jspark):
+        super(_SnowFlakeEmbeddingsLoader, self).__init__(
+            "com.johnsnowlabs.nlp.embeddings.SnowFlakeEmbeddings.loadSavedModel", path, jspark
+        )

python/test/annotator/embeddings/snowflake_embeddings_test.py

@@ -0,0 +1,50 @@
+#  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 unittest
+
+import pytest
+import os
+
+from sparknlp.annotator import *
+from sparknlp.base import *
+from test.util import SparkContextForTest
+
+
+@pytest.mark.slow
+class SnowFlakeEmbeddingsTestSpec(unittest.TestCase):
+	def setUp(self):
+		self.spark = SparkContextForTest.spark
+		self.tested_annotator = SnowFlakeEmbeddings \
+			.loadSavedModel("1",
+							SparkContextForTest.spark) \
+			.setInputCols(["documents"]) \
+			.setOutputCol("embeddings") \
+			.setPoolingStrategy("cls_avg")
+
+	def test_run(self):
+		data = SparkContextForTest.spark.read.option("header", "true") \
+			.csv(path="file:///" + os.getcwd() + "/../src/test/resources/embeddings/sentence_embeddings.csv")
+
+		document_assembler = DocumentAssembler() \
+			.setInputCol("text") \
+			.setOutputCol("documents")
+
+		embeddings_finisher = EmbeddingsFinisher().setInputCols("embeddings").setOutputCols("embeddings")
+
+		snowflake = self.tested_annotator
+
+		pipeline = Pipeline().setStages([document_assembler, snowflake, embeddings_finisher])
+		results = pipeline.fit(data).transform(data)
+
+		results.selectExpr("explode(embeddings) as result").show(truncate=False)

python/test/annotator/embeddings/uae_embeddings_test.py

@@ -25,8 +25,7 @@ class UAEEmbeddingsTestSpec(unittest.TestCase):
     def setUp(self):
         self.spark = SparkContextForTest.spark
         self.tested_annotator = UAEEmbeddings \
-            .loadSavedModel("/home/ducha/Workspace/JSL/spark-nlp-dev-things/hf_exports/UAE/exported_onnx",
-                            SparkContextForTest.spark) \
+            .pretrained() \
             .setInputCols(["documents"]) \
             .setOutputCol("embeddings") \
             .setPoolingStrategy("cls_avg")