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"""Contains classes for DeBertaEmbeddings."""
from sparknlp.common import *
[docs]class DeBertaEmbeddings(AnnotatorModel,
HasEmbeddingsProperties,
HasCaseSensitiveProperties,
HasStorageRef,
HasBatchedAnnotate,
HasEngine,
HasMaxSentenceLengthLimit):
"""The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with
Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu
Chen It is based on Google’s BERT model released in 2018 and Facebook’s
RoBERTa model released in 2019.
This model requires input tokenization with
SentencePiece model, which is provided by Spark NLP (See tokenizers
package).
It builds on RoBERTa with disentangled attention and enhanced mask decoder
training with half of the data used in RoBERTa.
Pretrained models can be loaded with pretrained of the companion object:
>>> embeddings = DeBertaEmbeddings.pretrained() \\
... .setInputCols(["sentence", "token"]) \\
... .setOutputCol("embeddings")
The default model is ``"deberta_v3_base"``, if no name is provided.
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
False
configProtoBytes
ConfigProto from tensorflow, serialized into byte array.
maxSentenceLength
Max sentence length to process, by default 128
References
----------
https://github.com/microsoft/DeBERTa
https://www.microsoft.com/en-us/research/blog/microsoft-deberta-surpasses-human-performance-on-the-superglue-benchmark/
**Paper abstract:**
*Paper abstract: Recent progress in pre-trained neural language models has
significantly improved the performance of many natural language processing
(NLP) tasks. In this paper we propose a new model architecture DeBERTa (
Decoding-enhanced BERT with disentangled attention) that improves the BERT
and RoBERTa models using two novel techniques. The first is the disentangled
attention mechanism, where each word is represented using two vectors that
encode its content and position, respectively, and the attention weights
among words are computed using disentangled matrices on their contents and
relative positions. Second, an enhanced mask decoder is used to replace the
output softmax layer to predict the masked tokens for model pretraining. We
show that these two techniques significantly improve the efficiency of model
pretraining and performance of downstream tasks. Compared to RoBERTa-Large,
a DeBERTa model trained on half of the training data performs consistently
better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9%
(90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by
+3.6% (83.2% vs. 86.8%). The DeBERTa code and pre-trained models will be
made publicly available at https://github.com/microsoft/DeBERTa.*
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"]) \\
>>> embeddings = DeBertaEmbeddings.pretrained() \\
... .setInputCols(["token", "document"]) \\
... .setOutputCol("embeddings")
>>> 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|
+--------------------------------------------------------------------------------+
|[1.1342473030090332,-1.3855540752410889,0.9818322062492371,-0.784737348556518...|
|[0.847029983997345,-1.047153353691101,-0.1520637571811676,-0.6245765686035156...|
|[-0.009860038757324219,-0.13450059294700623,2.707749128341675,1.2916892766952...|
|[-0.04192575812339783,-0.5764210224151611,-0.3196685314178467,-0.527840495109...|
|[0.15583214163780212,-0.1614152491092682,-0.28423872590065,-0.135491415858268...|
+--------------------------------------------------------------------------------+
"""
name = "DeBertaEmbeddings"
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.DeBertaEmbeddings", java_model=None):
super(DeBertaEmbeddings, self).__init__(
classname=classname,
java_model=java_model
)
self._setDefault(
batchSize=8,
dimension=768,
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
-------
DeBertaEmbeddings
The restored model
"""
from sparknlp.internal import _DeBERTaLoader
jModel = _DeBERTaLoader(folder, spark_session._jsparkSession)._java_obj
return DeBertaEmbeddings(java_model=jModel)
@staticmethod
[docs] def pretrained(name="deberta_v3_base", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default "deberta_v3_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
-------
DeBertaEmbeddings
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(DeBertaEmbeddings, name, lang, remote_loc)