Source code for sparknlp.annotator.classifier_dl.longformer_for_token_classification

#  Copyright 2017-2022 John Snow Labs
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"""Contains classes for LongformerForTokenClassification."""

from sparknlp.common import *


[docs]class LongformerForTokenClassification(AnnotatorModel, HasCaseSensitiveProperties, HasBatchedAnnotate, HasEngine, HasLongMaxSentenceLengthLimit): """LongformerForTokenClassification can load Longformer Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. Pretrained models can be loaded with :meth:`.pretrained` of the companion object: >>> token_classifier = LongformerForTokenClassification.pretrained() \\ ... .setInputCols(["token", "document"]) \\ ... .setOutputCol("label") The default model is ``"xlnet_base_token_classifier_conll03"``, if no name is provided. For available pretrained models please see the `Models Hub <https://sparknlp.org/models?task=Named+Entity+Recognition>`__. 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`` ``NAMED_ENTITY`` ====================== ====================== Parameters ---------- batchSize Batch size. Large values allows faster processing but requires more memory, by default 8 caseSensitive Whether to ignore case in tokens for embeddings matching, by default True configProtoBytes ConfigProto from tensorflow, serialized into byte array. maxSentenceLength Max sentence length to process, by default 128 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") >>> tokenClassifier = LongformerForTokenClassification.pretrained() \\ ... .setInputCols(["token", "document"]) \\ ... .setOutputCol("label") \\ ... .setCaseSensitive(True) >>> pipeline = Pipeline().setStages([ ... documentAssembler, ... tokenizer, ... tokenClassifier ... ]) >>> data = spark.createDataFrame([["John Lenon was born in London and lived in Paris. My name is Sarah and I live in London"]]).toDF("text") >>> result = pipeline.fit(data).transform(data) >>> result.select("label.result").show(truncate=False) +------------------------------------------------------------------------------------+ |result | +------------------------------------------------------------------------------------+ |[B-PER, I-PER, O, O, O, B-LOC, O, O, O, B-LOC, O, O, O, O, B-PER, O, O, O, O, B-LOC]| +------------------------------------------------------------------------------------+ """ name = "LongformerForTokenClassification" inputAnnotatorTypes = [AnnotatorType.DOCUMENT, AnnotatorType.TOKEN] outputAnnotatorType = AnnotatorType.NAMED_ENTITY configProtoBytes = Param(Params._dummy(), "configProtoBytes", "ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()", TypeConverters.toListInt)
[docs] def getClasses(self): """ Returns labels used to train this model """ return self._call_java("getClasses")
[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.annotators.classifier.dl.LongformerForTokenClassification", java_model=None): super(LongformerForTokenClassification, self).__init__( classname=classname, java_model=java_model ) self._setDefault( 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 ------- LongformerForTokenClassification The restored model """ from sparknlp.internal import _LongformerTokenClassifierLoader jModel = _LongformerTokenClassifierLoader(folder, spark_session._jsparkSession)._java_obj return LongformerForTokenClassification(java_model=jModel)
@staticmethod
[docs] def pretrained(name="longformer_base_token_classifier_conll03", lang="en", remote_loc=None): """Downloads and loads a pretrained model. Parameters ---------- name : str, optional Name of the pretrained model, by default "longformer_base_token_classifier_conll03" 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 ------- LongformerForTokenClassification The restored model """ from sparknlp.pretrained import ResourceDownloader return ResourceDownloader.downloadModel(LongformerForTokenClassification, name, lang, remote_loc)