# 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 E5Embeddings."""
from sparknlp.common import *
[docs]class MPNetEmbeddings(AnnotatorModel,
HasEmbeddingsProperties,
HasCaseSensitiveProperties,
HasStorageRef,
HasBatchedAnnotate,
HasMaxSentenceLengthLimit):
"""Sentence embeddings using MPNet.
MPNet adopts a novel pre-training method, named masked and permuted language modeling,
to inherit the advantages of masked language modeling and permuted language modeling for
natural language understanding.
Pretrained models can be loaded with :meth:`.pretrained` of the companion
object:
>>> embeddings = MPNetEmbeddings.pretrained() \\
... .setInputCols(["document"]) \\
... .setOutputCol("mpnet_embeddings")
The default model is ``"all_mpnet_base_v2"``, if no name is provided.
For available pretrained models please see the
`Models Hub <https://sparknlp.org/models?q=MPNet>`__.
====================== ======================
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
----------
`MPNet: Masked and Permuted Pre-training for Language Understanding <https://arxiv.org/pdf/2004.09297>`__
https://github.com/microsoft/MPNet
**Paper abstract**
*BERT adopts masked language modeling (MLM) for pre-training and is one of the most successful pre-training models.
Since BERT neglects dependency among predicted tokens, XLNet introduces permuted language modeling (PLM) for
pre-training to address this problem. However, XLNet does not leverage the full position information of a sentence
and thus suffers from position discrepancy between pre-training and fine-tuning. In this paper, we propose MPNet,
a novel pre-training method that inherits the advantages of BERT and XLNet and avoids their limitations. MPNet
leverages the dependency among predicted tokens through permuted language modeling (vs. MLM in BERT), and takes
auxiliary position information as input to make the model see a full sentence and thus reducing the position
discrepancy (vs. PLM in XLNet). We pre-train MPNet on a large-scale dataset (over 160GB text corpora) and fine-tune
on a variety of down-streaming tasks (GLUE, SQuAD, etc). Experimental results show that MPNet outperforms MLM and
PLM by a large margin, and achieves better results on these tasks compared with previous state-of-the-art
pre-trained methods (e.g., BERT, XLNet, RoBERTa) under the same model setting.*
Examples
--------
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \\
... .setInputCol("text") \\
... .setOutputCol("document")
>>> embeddings = MPNetEmbeddings.pretrained() \\
... .setInputCols(["document"]) \\
... .setOutputCol("mpnet_embeddings")
>>> embeddingsFinisher = EmbeddingsFinisher() \\
... .setInputCols(["mpnet_embeddings"]) \\
... .setOutputCols("finished_embeddings") \\
... .setOutputAsVector(True)
>>> pipeline = Pipeline().setStages([
... documentAssembler,
... embeddings,
... embeddingsFinisher
... ])
>>> data = spark.createDataFrame([["This is an example sentence", "Each sentence is converted"]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
+--------------------------------------------------------------------------------+
| result|
+--------------------------------------------------------------------------------+
|[[0.022502584, -0.078291744, -0.023030775, -0.0051000593, -0.080340415, 0.039...|
|[[0.041702367, 0.0010974605, -0.015534201, 0.07092203, -0.0017729357, 0.04661...|
+--------------------------------------------------------------------------------+
"""
name = "MPNetEmbeddings"
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.MPNetEmbeddings", java_model=None):
super(MPNetEmbeddings, 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
-------
MPNetEmbeddings
The restored model
"""
from sparknlp.internal import _MPNetLoader
jModel = _MPNetLoader(folder, spark_session._jsparkSession)._java_obj
return MPNetEmbeddings(java_model=jModel)
@staticmethod
[docs] def pretrained(name="all_mpnet_base_v2", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default "all_mpnet_base_v2"
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
-------
MPNetEmbeddings
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(MPNetEmbeddings, name, lang, remote_loc)