Source code for sparknlp.annotator.classifier_dl.xlm_roberta_for_sequence_classification

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"""Contains classes for XlmRoBertaForSequenceClassification."""

from sparknlp.common import *


[docs]class XlmRoBertaForSequenceClassification(AnnotatorModel, HasCaseSensitiveProperties, HasBatchedAnnotate, HasClassifierActivationProperties, HasEngine, HasMaxSentenceLengthLimit): """XlmRoBertaForSequenceClassification can load XLM-RoBERTa Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for multi-class document classification tasks. Pretrained models can be loaded with :meth:`.pretrained` of the companion object: >>> sequenceClassifier = XlmRoBertaForSequenceClassification.pretrained() \\ ... .setInputCols(["token", "document"]) \\ ... .setOutputCol("label") The default model is ``"xlm_roberta_base_sequence_classifier_imdb"``, if no name is provided. For available pretrained models please see the `Models Hub <https://sparknlp.org/models?task=Text+Classification>`__. 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`` ``CATEGORY`` ====================== ====================== 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 coalesceSentences Instead of 1 class per sentence (if inputCols is `sentence`) output 1 class per document by averaging probabilities in all sentences, by default False. activation Whether to calculate logits via Softmax or Sigmoid, by default `"softmax"`. 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") >>> sequenceClassifier = XlmRoBertaForSequenceClassification.pretrained() \\ ... .setInputCols(["token", "document"]) \\ ... .setOutputCol("label") \\ ... .setCaseSensitive(True) >>> pipeline = Pipeline().setStages([ ... documentAssembler, ... tokenizer, ... sequenceClassifier ... ]) >>> data = spark.createDataFrame([["I loved this movie when I was a child.", "It was pretty boring."]]).toDF("text") >>> result = pipeline.fit(data).transform(data) >>> result.select("label.result").show(truncate=False) +------+ |result| +------+ |[pos] | |[neg] | +------+ """ name = "XlmRoBertaForSequenceClassification" inputAnnotatorTypes = [AnnotatorType.DOCUMENT, AnnotatorType.TOKEN] outputAnnotatorType = AnnotatorType.CATEGORY configProtoBytes = Param(Params._dummy(), "configProtoBytes", "ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()", TypeConverters.toListInt) coalesceSentences = Param(Params._dummy(), "coalesceSentences", "Instead of 1 class per sentence (if inputCols is '''sentence''') output 1 class per document by averaging probabilities in all sentences.", TypeConverters.toBoolean)
[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)
[docs] def setCoalesceSentences(self, value): """Instead of 1 class per sentence (if inputCols is '''sentence''') output 1 class per document by averaging probabilities in all sentences. Due to max sequence length limit in almost all transformer models such as BERT (512 tokens), this parameter helps feeding all the sentences into the model and averaging all the probabilities for the entire document instead of probabilities per sentence. (Default: true) Parameters ---------- value : bool If the output of all sentences will be averaged to one output """ return self._set(coalesceSentences=value)
@keyword_only def __init__(self, classname="com.johnsnowlabs.nlp.annotators.classifier.dl.XlmRoBertaForSequenceClassification", java_model=None): super(XlmRoBertaForSequenceClassification, self).__init__( classname=classname, java_model=java_model ) self._setDefault( batchSize=8, maxSentenceLength=128, caseSensitive=True, coalesceSentences=False, activation="softmax" ) @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 ------- XlmRoBertaForSequenceClassification The restored model """ from sparknlp.internal import _XlmRoBertaSequenceClassifierLoader jModel = _XlmRoBertaSequenceClassifierLoader(folder, spark_session._jsparkSession)._java_obj return XlmRoBertaForSequenceClassification(java_model=jModel)
@staticmethod
[docs] def pretrained(name="xlm_roberta_base_sequence_classifier_imdb", lang="en", remote_loc=None): """Downloads and loads a pretrained model. Parameters ---------- name : str, optional Name of the pretrained model, by default "xlm_roberta_base_sequence_classifier_imdb" 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 ------- XlmRoBertaForSequenceClassification The restored model """ from sparknlp.pretrained import ResourceDownloader return ResourceDownloader.downloadModel(XlmRoBertaForSequenceClassification, name, lang, remote_loc)