# Copyright 2017-2022 John Snow Labs
#
# Licensed under the Apache License, Version 2.0 (the "License");
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#
# http://www.apache.org/licenses/LICENSE-2.0
#
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"""Contains classes for BGEEmbeddings."""
from sparknlp.common import *
[docs]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)
[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.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
[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
-------
BGEEmbeddings
The restored model
"""
from sparknlp.internal import _BGELoader
jModel = _BGELoader(folder, spark_session._jsparkSession)._java_obj
return BGEEmbeddings(java_model=jModel)
@staticmethod
[docs] 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)