Added BGE Embeddings (#14090)

* Added BGE Embeddings

* Fixed class names

python/sparknlp/annotator/embeddings/bge_embeddings.py

@@ -0,0 +1,192 @@
+#  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 BGEEmbeddings."""
+
+from sparknlp.common import *
+
+
+class BGEEmbeddings(AnnotatorModel,
+                           HasEmbeddingsProperties,
+                           HasCaseSensitiveProperties,
+                           HasStorageRef,
+                           HasBatchedAnnotate,
+                           HasMaxSentenceLengthLimit):
+    """Sentence embeddings using BGE.
+
+   BGE, or BAAI General Embeddings, a model that can map any text to a low-dimensional dense 
+  vector which can be used for tasks like retrieval, classification, clustering, or semantic search.
+  
+  Pretrained models can be loaded with `pretrained` of the companion object:
+
+    >>> embeddings = BGEEmbeddings.pretrained() \\
+    ...     .setInputCols(["document"]) \\
+    ...     .setOutputCol("bge_embeddings")
+
+
+    The default model is ``"bge_base"``, if no name is provided.
+
+    For available pretrained models please see the
+    `Models Hub <https://sparknlp.org/models?q=BGE>`__.
+
+
+    ====================== ======================
+    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
+    ----------
+    `C-Pack: Packaged Resources To Advance General Chinese Embedding <https://arxiv.org/pdf/2309.07597>`__
+    `BGE Github Repository <https://github.com/FlagOpen/FlagEmbedding>`__
+
+    **Paper abstract**
+
+    *We introduce C-Pack, a package of resources that significantly advance the field of general
+    Chinese embeddings. C-Pack includes three critical resources. 
+    1) C-MTEB is a comprehensive benchmark for Chinese text embeddings covering 6 tasks and 35 datasets.
+    2) C-MTP is a massive text embedding dataset curated from labeled and unlabeled Chinese corpora
+    for training embedding models.
+    3) C-TEM is a family of embedding models covering multiple sizes.
+    Our models outperform all prior Chinese text embeddings on C-MTEB by up to +10% upon the 
+    time of the release. We also integrate and optimize the entire suite of training methods for
+    C-TEM. Along with our resources on general Chinese embedding, we release our data and models for
+    English text embeddings. The English models achieve stateof-the-art performance on the MTEB
+    benchmark; meanwhile, our released English data is 2 times larger than the Chinese data. All
+    these resources are made publicly available at https://github.com/FlagOpen/FlagEmbedding.*
+
+    Examples
+    --------
+    >>> import sparknlp
+    >>> from sparknlp.base import *
+    >>> from sparknlp.annotator import *
+    >>> from pyspark.ml import Pipeline
+    >>> documentAssembler = DocumentAssembler() \\
+    ...     .setInputCol("text") \\
+    ...     .setOutputCol("document")
+    >>> embeddings = BGEEmbeddings.pretrained() \\
+    ...     .setInputCols(["document"]) \\
+    ...     .setOutputCol("bge_embeddings")
+    >>> embeddingsFinisher = EmbeddingsFinisher() \\
+    ...     .setInputCols(["bge_embeddings"]) \\
+    ...     .setOutputCols("finished_embeddings") \\
+    ...     .setOutputAsVector(True)
+    >>> pipeline = Pipeline().setStages([
+    ...     documentAssembler,
+    ...     embeddings,
+    ...     embeddingsFinisher
+    ... ])
+    >>> data = spark.createDataFrame([["query: how much protein should a female eat",
+    ... "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day." + \
+    ... "But, as you can see from this chart, you'll need to increase that if you're expecting or training for a" + \
+    ... "marathon. Check out the chart below to see how much protein you should be eating each day.",
+    ... ]]).toDF("text")
+    >>> result = pipeline.fit(data).transform(data)
+    >>> result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
+    +--------------------------------------------------------------------------------+
+    |                                                                          result|
+    +--------------------------------------------------------------------------------+
+    |[[8.0190285E-4, -0.005974853, -0.072875895, 0.007944068, 0.026059335, -0.0080...|
+    |[[0.050514214, 0.010061974, -0.04340176, -0.020937217, 0.05170225, 0.01157857...|
+    +--------------------------------------------------------------------------------+
+    """
+
+    name = "BGEEmbeddings"
+
+    inputAnnotatorTypes = [AnnotatorType.DOCUMENT]
+
+    outputAnnotatorType = AnnotatorType.SENTENCE_EMBEDDINGS
+    configProtoBytes = Param(Params._dummy(),
+                             "configProtoBytes",
+                             "ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()",
+                             TypeConverters.toListInt)
+
+
+    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.BGEEmbeddings", java_model=None):
+        super(BGEEmbeddings, self).__init__(
+            classname=classname,
+            java_model=java_model
+        )
+        self._setDefault(
+            dimension=768,
+            batchSize=8,
+            maxSentenceLength=512,
+            caseSensitive=False,
+        )
+
+    @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
+        -------
+        BGEEmbeddings
+            The restored model
+        """
+        from sparknlp.internal import _BGELoader
+        jModel = _BGELoader(folder, spark_session._jsparkSession)._java_obj
+        return BGEEmbeddings(java_model=jModel)
+
+    @staticmethod
+    def pretrained(name="bge_base", lang="en", remote_loc=None):
+        """Downloads and loads a pretrained model.
+
+        Parameters
+        ----------
+        name : str, optional
+            Name of the pretrained model, by default "bge_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
+        -------
+        BGEEmbeddings
+            The restored model
+        """
+        from sparknlp.pretrained import ResourceDownloader
+        return ResourceDownloader.downloadModel(BGEEmbeddings, name, lang, remote_loc)

python/sparknlp/internal/__init__.py

@@ -147,6 +147,9 @@ class _E5Loader(ExtendedJavaWrapper):
     def __init__(self, path, jspark):
         super(_E5Loader, self).__init__("com.johnsnowlabs.nlp.embeddings.E5Embeddings.loadSavedModel", path, jspark)
 
+class _BGELoader(ExtendedJavaWrapper):
+    def __init__(self, path, jspark):
+        super(_BGELoader, self).__init__("com.johnsnowlabs.nlp.embeddings.BGEEmbeddings.loadSavedModel", path, jspark)
 
 class _GPT2Loader(ExtendedJavaWrapper):
     def __init__(self, path, jspark):