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"""Contains classes for RoBertaEmbeddings."""
from sparknlp.common import *
[docs]class RoBertaEmbeddings(AnnotatorModel,
HasEmbeddingsProperties,
HasCaseSensitiveProperties,
HasStorageRef,
HasBatchedAnnotate,
HasEngine,
HasMaxSentenceLengthLimit):
"""Creates word 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 :meth:`.pretrained` of the companion
object:
>>> embeddings = RoBertaEmbeddings.pretrained() \\
... .setInputCols(["document", "token"]) \\
... .setOutputCol("embeddings")
The default model is ``"roberta_base"``, if no name is provided. For
available pretrained models please see the `Models Hub
<https://sparknlp.org/models?task=Embeddings>`__.
For extended examples of usage, see the `Examples
<https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20RoBERTa.ipynb>`__.
To see which models are compatible and how to import them see
`Import Transformers into Spark NLP 🚀
<https://github.com/JohnSnowLabs/spark-nlp/discussions/5669>`_.
====================== ======================
Input Annotation types Output Annotation type
====================== ======================
``DOCUMENT, TOKEN`` ``WORD_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
True
maxSentenceLength
Max sentence length to process, by default 128
configProtoBytes
ConfigProto from tensorflow, serialized into byte array.
Notes
-----
- RoBERTa has the same architecture as BERT, but uses a byte-level BPE as
a tokenizer (same as GPT-2) and uses a different pretraining scheme.
- RoBERTa doesn't have ``token_type_ids``, you don't need to indicate
which token belongs to which segment. Just separate your segments with
the separation token ``tokenizer.sep_token`` (or ``</s>``)
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.*
Source of the original code: `RoBERTa: A Robustly Optimized BERT Pretraining
Approach on GitHub
<https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__.
Examples
--------
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \\
... .setInputCol("text") \\
... .setOutputCol("document")
>>> tokenizer = Tokenizer() \\
... .setInputCols(["document"]) \\
... .setOutputCol("token")
>>> embeddings = RoBertaEmbeddings.pretrained() \\
... .setInputCols(["document", "token"]) \\
... .setOutputCol("embeddings") \\
... .setCaseSensitive(True)
>>> embeddingsFinisher = EmbeddingsFinisher() \\
... .setInputCols(["embeddings"]) \\
... .setOutputCols("finished_embeddings") \\
... .setOutputAsVector(True) \\
... .setCleanAnnotations(False)
>>> pipeline = Pipeline() \\
... .setStages([
... documentAssembler,
... tokenizer,
... embeddings,
... embeddingsFinisher
... ])
>>> data = spark.createDataFrame([["This is a sentence."]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
+--------------------------------------------------------------------------------+
| result|
+--------------------------------------------------------------------------------+
|[0.18792399764060974,-0.14591649174690247,0.20547787845134735,0.1468472778797...|
|[0.22845706343650818,0.18073144555091858,0.09725798666477203,-0.0417917296290...|
|[0.07037967443466187,-0.14801117777824402,-0.03603338822722435,-0.17893412709...|
|[-0.08734266459941864,0.2486150562763214,-0.009067727252840996,-0.24408400058...|
|[0.22409197688102722,-0.4312366545200348,0.1401449590921402,0.356410235166549...|
+--------------------------------------------------------------------------------+
"""
name = "RoBertaEmbeddings"
inputAnnotatorTypes = [AnnotatorType.DOCUMENT, AnnotatorType.TOKEN]
outputAnnotatorType = AnnotatorType.WORD_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.RoBertaEmbeddings", java_model=None):
super(RoBertaEmbeddings, 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
-------
RoBertaEmbeddings
The restored model
"""
from sparknlp.internal import _RoBertaLoader
jModel = _RoBertaLoader(folder, spark_session._jsparkSession)._java_obj
return RoBertaEmbeddings(java_model=jModel)
@staticmethod
[docs] def pretrained(name="roberta_base", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default "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
-------
RoBertaEmbeddings
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(RoBertaEmbeddings, name, lang, remote_loc)