sparknlp.annotator.classifier_dl.roberta_for_sequence_classification
#
Contains classes for RoBertaForSequenceClassification.
Module Contents#
Classes#
RoBertaForSequenceClassification can load RoBERTa Models with sequence classification/regression head on |
- class RoBertaForSequenceClassification(classname='com.johnsnowlabs.nlp.annotators.classifier.dl.RoBertaForSequenceClassification', java_model=None)[source]#
RoBertaForSequenceClassification can load 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
pretrained()
of the companion object:>>> sequenceClassifier = RoBertaForSequenceClassification.pretrained() \ ... .setInputCols(["token", "document"]) \ ... .setOutputCol("label")
The default model is
"roberta_base_sequence_classifier_imdb"
, 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 🚀.
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 = RoBertaForSequenceClassification.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] | +------+
- setConfigProtoBytes(b)[source]#
Sets configProto from tensorflow, serialized into byte array.
- Parameters:
- bList[int]
ConfigProto from tensorflow, serialized into byte array
- setCoalesceSentences(value)[source]#
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:
- valuebool
If the output of all sentences will be averaged to one output
- 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:
- RoBertaForSequenceClassification
The restored model
- static pretrained(name='roberta_base_sequence_classifier_imdb', lang='en', remote_loc=None)[source]#
Downloads and loads a pretrained model.
- Parameters:
- namestr, optional
Name of the pretrained model, by default “roberta_base_sequence_classifier_imdb”
- langstr, optional
Language of the pretrained model, by default “en”
- remote_locstr, optional
Optional remote address of the resource, by default None. Will use Spark NLPs repositories otherwise.
- Returns:
- RoBertaForSequenceClassification
The restored model