# 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
#
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"""Contains classes for RoBertaSentenceEmbeddings."""
from sparknlp.common import *
[docs]class RoBertaSentenceEmbeddings(AnnotatorModel,
HasEmbeddingsProperties,
HasCaseSensitiveProperties,
HasStorageRef,
HasBatchedAnnotate,
HasEngine,
HasMaxSentenceLengthLimit):
"""Sentence-level embeddings using RoBERTa. The RoBERTa model was proposed in RoBERTa: A Robustly Optimized BERT
Pretraining Approach by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy,
Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. It is based on Google's BERT model released in 2018. It builds on
BERT and modifies key hyperparameters, removing the next-sentence pretraining objective and training with much
larger mini-batches and learning rates. Pretrained models can be loaded with pretrained of the companion object:
Pretrained models can be loaded with :meth:`.pretrained` of the companion
object:
>>> embeddings = RoBertaSentenceEmbeddings.pretrained() \\
... .setInputCols(["sentence"]) \\
... .setOutputCol("sentence_embeddings")
The default model is ``"sent_roberta_base"``, if no name is provided.
For available pretrained models please see the
`Models Hub <https://sparknlp.org/models?task=Embeddings>`__.
====================== =======================
Input Annotation types Output Annotation type
====================== =======================
``DOCUMENT`` ``SENTENCE_EMBEDDINGS``
====================== =======================
Parameters
----------
batchSize
Size of every batch, by default 8
caseSensitive
Whether to ignore case in tokens for embeddings matching, by default
False
dimension
Number of embedding dimensions, by default 768
maxSentenceLength
Max sentence length to process, by default 128
configProtoBytes
ConfigProto from tensorflow, serialized into byte array.
References
----------
`RoBERTa: A Robustly Optimized BERT Pretraining Approach <https://arxiv.org/abs/1907.11692>`__
**Paper abstract:**
*Language model pretraining has led to significant performance gains but careful comparison between different
approaches is challenging. Training is computationally expensive, often done on private datasets of different
sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a
replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key
hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed
the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE,
RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise
questions about the source of recently reported improvements. We release our models and code.*
Examples
--------
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \\
... .setInputCol("text") \\
... .setOutputCol("document")
>>> sentence = SentenceDetector() \\
... .setInputCols(["document"]) \\
... .setOutputCol("sentence")
>>> embeddings = RoBertaSentenceEmbeddings.pretrained() \\
... .setInputCols(["sentence"]) \\
... .setOutputCol("sentence_embeddings")
>>> embeddingsFinisher = EmbeddingsFinisher() \\
... .setInputCols(["sentence_embeddings"]) \\
... .setOutputCols("finished_embeddings") \\
... .setOutputAsVector(True)
>>> pipeline = Pipeline().setStages([
... documentAssembler,
... sentence,
... embeddings,
... embeddingsFinisher
... ])
>>> data = spark.createDataFrame([["John loves apples. Mary loves oranges. John loves Mary."]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
+--------------------------------------------------------------------------------+
| result|
+--------------------------------------------------------------------------------+
|[-0.8951074481010437,0.13753940165042877,0.3108254075050354,-1.65693199634552...|
|[-0.6180210709571838,-0.12179657071828842,-0.191165953874588,-1.4497021436691...|
|[-0.822715163230896,0.7568016648292542,-0.1165061742067337,-1.59048593044281,...|
+--------------------------------------------------------------------------------+
"""
name = "RoBertaSentenceEmbeddings"
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.RoBertaSentenceEmbeddings", java_model=None):
super(RoBertaSentenceEmbeddings, self).__init__(
classname=classname,
java_model=java_model
)
self._setDefault(
dimension=768,
batchSize=8,
maxSentenceLength=128,
caseSensitive=True
)
@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
-------
BertSentenceEmbeddings
The restored model
"""
from sparknlp.internal import _RoBertaSentenceLoader
jModel = _RoBertaSentenceLoader(folder, spark_session._jsparkSession)._java_obj
return RoBertaSentenceEmbeddings(java_model=jModel)
@staticmethod
[docs] def pretrained(name="sent_roberta_base", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default "sent_roberta_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
-------
RoBertaSentenceEmbeddings
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(RoBertaSentenceEmbeddings, name, lang, remote_loc)