sparknlp.annotator.classifier_dl.xlm_roberta_for_question_answering
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Module Contents#
Classes#
XlmRoBertaForQuestionAnswering can load XLM-RoBERTa Models with a span classification head on top for extractive |
- class XlmRoBertaForQuestionAnswering(classname='com.johnsnowlabs.nlp.annotators.classifier.dl.XlmRoBertaForQuestionAnswering', java_model=None)[source]#
XlmRoBertaForQuestionAnswering can load XLM-RoBERTa Models with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits).
Pretrained models can be loaded with
pretrained()
of the companion object:>>> spanClassifier = XlmRoBertaForQuestionAnswering.pretrained() \ ... .setInputCols(["document_question", "document_context"]) \ ... .setOutputCol("answer")
The default model is
"xlm_roberta_base_qa_squad2"
, if no name is provided.For available pretrained models please see the Models Hub.
To see which models are compatible and how to import them see Import Transformers into Spark NLP π.
Input Annotation types
Output Annotation type
DOCUMENT, DOCUMENT
CHUNK
- 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 False
- 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 = MultiDocumentAssembler() \ ... .setInputCols(["question", "context"]) \ ... .setOutputCol(["document_question", "document_context"]) >>> spanClassifier = XlmRoBertaForQuestionAnswering.pretrained() \ ... .setInputCols(["document_question", "document_context"]) \ ... .setOutputCol("answer") \ ... .setCaseSensitive(False) >>> pipeline = Pipeline().setStages([ ... documentAssembler, ... spanClassifier ... ]) >>> data = spark.createDataFrame([["What's my name?", "My name is Clara and I live in Berkeley."]]).toDF("question", "context") >>> result = pipeline.fit(data).transform(data) >>> result.select("answer.result").show(truncate=False) +--------------------+ |result | +--------------------+ |[Clara] | +--------------------+
- setConfigProtoBytes(self, b)[source]#
Sets configProto from tensorflow, serialized into byte array.
- Parameters:
- bList[int]
ConfigProto from tensorflow, serialized into byte array
- setMaxSentenceLength(self, value)[source]#
Sets max sentence length to process, by default 128.
- Parameters:
- valueint
Max sentence length to process
- static loadSavedModel(folder, spark_session)[source]#
Loads a locally saved model.
- Parameters:
- folderstr
Folder of the saved model
- spark_sessionpyspark.sql.SparkSession
The current SparkSession
- Returns:
- XlmRoBertaForQuestionAnswering
The restored model
- static pretrained(name='xlm_roberta_base_qa_squad2', lang='en', remote_loc=None)[source]#
Downloads and loads a pretrained model.
- Parameters:
- namestr, optional
Name of the pretrained model, by default βxlm_roberta_base_qa_squad2β
- langstr, optional
Language of the pretrained model, by default βenβ
- remote_locstr, optional
Optional remote address of the resource, by default None. Will use Spark NLPs repositories otherwise.
- Returns:
- XlmRoBertaForQuestionAnswering
The restored model