Source code for sparknlp.annotator.embeddings.bert_embeddings

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"""Contains classes for BertEmbeddings."""

from sparknlp.common import *


[docs]class BertEmbeddings(AnnotatorModel, HasEmbeddingsProperties, HasCaseSensitiveProperties, HasStorageRef, HasBatchedAnnotate, HasMaxSentenceLengthLimit): """Token-level embeddings using BERT. BERT (Bidirectional Encoder Representations from Transformers) provides dense vector representations for natural language by using a deep, pre-trained neural network with the Transformer architecture. Pretrained models can be loaded with :meth:`.pretrained` of the companion object: >>> embeddings = BertEmbeddings.pretrained() \\ ... .setInputCols(["token", "document"]) \\ ... .setOutputCol("bert_embeddings") The default model is ``"small_bert_L2_768"``, 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/training/english/dl-ner/ner_bert.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 False maxSentenceLength Max sentence length to process, by default 128 configProtoBytes ConfigProto from tensorflow, serialized into byte array. References ---------- `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`__ https://github.com/google-research/bert **Paper abstract** *We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).* 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 = BertEmbeddings.pretrained("small_bert_L2_128", "en") \\ ... .setInputCols(["token", "document"]) \\ ... .setOutputCol("bert_embeddings") >>> embeddingsFinisher = EmbeddingsFinisher() \\ ... .setInputCols(["bert_embeddings"]) \\ ... .setOutputCols("finished_embeddings") \\ ... .setOutputAsVector(True) >>> 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| +--------------------------------------------------------------------------------+ |[-2.3497989177703857,0.480538547039032,-0.3238905668258667,-1.612930893898010...| |[-2.1357314586639404,0.32984697818756104,-0.6032363176345825,-1.6791689395904...| |[-1.8244884014129639,-0.27088963985443115,-1.059438943862915,-0.9817547798156...| |[-1.1648050546646118,-0.4725411534309387,-0.5938255786895752,-1.5780693292617...| |[-0.9125322699546814,0.4563939869403839,-0.3975459933280945,-1.81611204147338...| +--------------------------------------------------------------------------------+ """ name = "BertEmbeddings" 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.BertEmbeddings", java_model=None): super(BertEmbeddings, self).__init__( classname=classname, java_model=java_model ) self._setDefault( dimension=768, batchSize=8, maxSentenceLength=128, 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 ------- BertEmbeddings The restored model """ from sparknlp.internal import _BertLoader jModel = _BertLoader(folder, spark_session._jsparkSession)._java_obj return BertEmbeddings(java_model=jModel)
@staticmethod
[docs] def pretrained(name="small_bert_L2_768", lang="en", remote_loc=None): """Downloads and loads a pretrained model. Parameters ---------- name : str, optional Name of the pretrained model, by default "small_bert_L2_768" 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 ------- BertEmbeddings The restored model """ from sparknlp.pretrained import ResourceDownloader return ResourceDownloader.downloadModel(BertEmbeddings, name, lang, remote_loc)