com.johnsnowlabs.nlp.annotators.classifier

dl

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class AlbertForQuestionAnswering extends AnnotatorModel[AlbertForQuestionAnswering] with HasBatchedAnnotate[AlbertForQuestionAnswering] with WriteTensorflowModel with WriteSentencePieceModel with HasCaseSensitiveProperties
AlbertForQuestionAnswering can load ALBERT 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).
AlbertForQuestionAnswering can load ALBERT 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:
```
val spanClassifier = AlbertForQuestionAnswering.pretrained()
  .setInputCols(Array("document_question", "document_context"))
  .setOutputCol("answer")
```
The default model is "albert_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 https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see AlbertForQuestionAnsweringTestSpec.
Example
```
import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val document = new MultiDocumentAssembler()
  .setInputCols("question", "context")
  .setOutputCols("document_question", "document_context")

val questionAnswering = AlbertForQuestionAnswering.pretrained()
  .setInputCols(Array("document_question", "document_context"))
  .setOutputCol("answer")
  .setCaseSensitive(false)

val pipeline = new Pipeline().setStages(Array(
  document,
  questionAnswering
))

val data = Seq("What's my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(false)
+---------------------+
|result               |
+---------------------+
|[Clara]              |
++--------------------+
```
See also
AlbertForSequenceClassification for sequence-level classification
Annotators Main Page for a list of transformer based classifiers
class AlbertForSequenceClassification extends AnnotatorModel[AlbertForSequenceClassification] with HasBatchedAnnotate[AlbertForSequenceClassification] with WriteTensorflowModel with WriteSentencePieceModel with HasCaseSensitiveProperties with HasClassifierActivationProperties
AlbertForSequenceClassification can load ALBERT Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g.
AlbertForSequenceClassification can load ALBERT 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:
```
val sequenceClassifier = AlbertForSequenceClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")
```
The default model is "albert_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 https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see AlbertForSequenceClassification.
Example
```
import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val tokenizer = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")

val sequenceClassifier = AlbertForSequenceClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")
  .setCaseSensitive(true)

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  tokenizer,
  sequenceClassifier
))

val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(false)
+--------------------+
|result              |
+--------------------+
|[neg, neg]          |
|[pos, pos, pos, pos]|
+--------------------+
```
See also
AlbertForSequenceClassification for sequence-level classification
Annotators Main Page for a list of transformer based classifiers

class AlbertForTokenClassification extends AnnotatorModel[AlbertForTokenClassification] with HasBatchedAnnotate[AlbertForTokenClassification] with WriteTensorflowModel with WriteSentencePieceModel with HasCaseSensitiveProperties

AlbertForTokenClassification can load ALBERT Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g.

AlbertForTokenClassification can load ALBERT 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 pretrained of the companion object:

val tokenClassifier = AlbertForTokenClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")

The default model is "albert_base_token_classifier_conll03", if no name is provided.

For available pretrained models please see the Models Hub.

and the AlbertForTokenClassificationTestSpec. To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val tokenizer = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")

val tokenClassifier = AlbertForTokenClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")
  .setCaseSensitive(true)

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  tokenizer,
  tokenClassifier
))

val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(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]|
+------------------------------------------------------------------------------------+

See also: AlbertForTokenClassification for token-level classification
Annotators Main Page for a list of transformer based classifiers

class BertForQuestionAnswering extends AnnotatorModel[BertForQuestionAnswering] with HasBatchedAnnotate[BertForQuestionAnswering] with WriteTensorflowModel with HasCaseSensitiveProperties
BertForQuestionAnswering can load Bert 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).
BertForQuestionAnswering can load Bert 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:
```
val spanClassifier = BertForQuestionAnswering.pretrained()
  .setInputCols(Array("document_question", "document_context"))
  .setOutputCol("answer")
```
The default model is "bert_base_cased_qa_squad2", if no name is provided.
For available pretrained models please see the Models Hub.
Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. The Spark NLP Workshop example shows how to import them https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see BertForQuestionAnsweringTestSpec.
Example
```
import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val document = new MultiDocumentAssembler()
  .setInputCols("question", "context")
  .setOutputCols("document_question", "document_context")

val questionAnswering = BertForQuestionAnswering.pretrained()
  .setInputCols(Array("document_question", "document_context"))
  .setOutputCol("answer")
  .setCaseSensitive(true)

val pipeline = new Pipeline().setStages(Array(
  document,
  questionAnswering
))

val data = Seq("What's my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(false)
+---------------------+
|result               |
+---------------------+
|[Clara]              |
++--------------------+
```
See also
BertForSequenceClassification for span-level classification
Annotators Main Page for a list of transformer based classifiers
class BertForSequenceClassification extends AnnotatorModel[BertForSequenceClassification] with HasBatchedAnnotate[BertForSequenceClassification] with WriteTensorflowModel with HasCaseSensitiveProperties with HasClassifierActivationProperties
BertForSequenceClassification can load Bert Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g.
BertForSequenceClassification can load Bert 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:
```
val sequenceClassifier = BertForSequenceClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")
```
The default model is "bert_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 https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see BertForSequenceClassificationTestSpec.
Example
```
import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val tokenizer = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")

val sequenceClassifier = BertForSequenceClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")
  .setCaseSensitive(true)

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  tokenizer,
  sequenceClassifier
))

val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(false)
+--------------------+
|result              |
+--------------------+
|[neg, neg]          |
|[pos, pos, pos, pos]|
+--------------------+
```
See also
BertForSequenceClassification for sequnece-level classification
Annotators Main Page for a list of transformer based classifiers

class BertForTokenClassification extends AnnotatorModel[BertForTokenClassification] with HasBatchedAnnotate[BertForTokenClassification] with WriteTensorflowModel with HasCaseSensitiveProperties

BertForTokenClassification can load Bert Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g.

BertForTokenClassification can load Bert 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 pretrained of the companion object:

val tokenClassifier = BertForTokenClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")

The default model is "bert_base_token_classifier_conll03", 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 https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see BertForTokenClassificationTestSpec.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val tokenizer = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")

val tokenClassifier = BertForTokenClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")
  .setCaseSensitive(true)

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  tokenizer,
  tokenClassifier
))

val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(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]|
+------------------------------------------------------------------------------------+

See also: BertForTokenClassification for token-level classification
Annotators Main Page for a list of transformer based classifiers

class ClassifierDLApproach extends AnnotatorApproach[ClassifierDLModel] with ParamsAndFeaturesWritable

Trains a ClassifierDL for generic Multi-class Text Classification.

ClassifierDL uses the state-of-the-art Universal Sentence Encoder as an input for text classifications. The ClassifierDL annotator uses a deep learning model (DNNs) we have built inside TensorFlow and supports up to 100 classes.

For instantiated/pretrained models, see ClassifierDLModel.

Notes:

This annotator accepts a label column of a single item in either type of String, Int, Float, or Double.
UniversalSentenceEncoder, BertSentenceEmbeddings, or SentenceEmbeddings can be used for the inputCol.

For extended examples of usage, see the Spark NLP Workshop [1] [2] and the ClassifierDLTestSpec.

Example

In this example, the training data "sentiment.csv" has the form of

text,label
This movie is the best movie I have wached ever! In my opinion this movie can win an award.,0
This was a terrible movie! The acting was bad really bad!,1
...

Then traning can be done like so:

import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.embeddings.UniversalSentenceEncoder
import com.johnsnowlabs.nlp.annotators.classifier.dl.ClassifierDLApproach
import org.apache.spark.ml.Pipeline

val smallCorpus = spark.read.option("header","true").csv("src/test/resources/classifier/sentiment.csv")

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val useEmbeddings = UniversalSentenceEncoder.pretrained()
  .setInputCols("document")
  .setOutputCol("sentence_embeddings")

val docClassifier = new ClassifierDLApproach()
  .setInputCols("sentence_embeddings")
  .setOutputCol("category")
  .setLabelColumn("label")
  .setBatchSize(64)
  .setMaxEpochs(20)
  .setLr(5e-3f)
  .setDropout(0.5f)

val pipeline = new Pipeline()
  .setStages(
    Array(
      documentAssembler,
      useEmbeddings,
      docClassifier
    )
  )

val pipelineModel = pipeline.fit(smallCorpus)

See also: MultiClassifierDLApproach for multi-class classification
SentimentDLApproach for sentiment analysis

class ClassifierDLModel extends AnnotatorModel[ClassifierDLModel] with HasSimpleAnnotate[ClassifierDLModel] with WriteTensorflowModel with HasStorageRef with ParamsAndFeaturesWritable

ClassifierDL for generic Multi-class Text Classification.

This is the instantiated model of the ClassifierDLApproach. For training your own model, please see the documentation of that class.

Pretrained models can be loaded with pretrained of the companion object:

val classifierDL = ClassifierDLModel.pretrained()
  .setInputCols("sentence_embeddings")
  .setOutputCol("classification")

The default model is "classifierdl_use_trec6", if no name is provided. It uses embeddings from the UniversalSentenceEncoder and is trained on the TREC-6 dataset. For available pretrained models please see the Models Hub.

For extended examples of usage, see the Spark NLP Workshop and the ClassifierDLTestSpec.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotator.SentenceDetector
import com.johnsnowlabs.nlp.annotators.classifier.dl.ClassifierDLModel
import com.johnsnowlabs.nlp.embeddings.UniversalSentenceEncoder
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val sentence = new SentenceDetector()
  .setInputCols("document")
  .setOutputCol("sentence")

val useEmbeddings = UniversalSentenceEncoder.pretrained()
  .setInputCols("document")
  .setOutputCol("sentence_embeddings")

val sarcasmDL = ClassifierDLModel.pretrained("classifierdl_use_sarcasm")
  .setInputCols("sentence_embeddings")
  .setOutputCol("sarcasm")

val pipeline = new Pipeline()
  .setStages(Array(
    documentAssembler,
    sentence,
    useEmbeddings,
    sarcasmDL
  ))

val data = Seq(
  "I'm ready!",
  "If I could put into words how much I love waking up at 6 am on Mondays I would."
).toDF("text")
val result = pipeline.fit(data).transform(data)

result.selectExpr("explode(arrays_zip(sentence, sarcasm)) as out")
  .selectExpr("out.sentence.result as sentence", "out.sarcasm.result as sarcasm")
  .show(false)
+-------------------------------------------------------------------------------+-------+
|sentence                                                                       |sarcasm|
+-------------------------------------------------------------------------------+-------+
|I'm ready!                                                                     |normal |
|If I could put into words how much I love waking up at 6 am on Mondays I would.|sarcasm|
+-------------------------------------------------------------------------------+-------+

See also: MultiClassifierDLModel for multi-class classification
SentimentDLModel for sentiment analysis

class DeBertaForQuestionAnswering extends AnnotatorModel[DeBertaForQuestionAnswering] with HasBatchedAnnotate[DeBertaForQuestionAnswering] with WriteTensorflowModel with WriteSentencePieceModel with HasCaseSensitiveProperties
DeBertaForQuestionAnswering can load DeBERTa 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).
DeBertaForQuestionAnswering can load DeBERTa 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:
```
val spanClassifier = DeBertaForQuestionAnswering.pretrained()
  .setInputCols(Array("document_question", "document_context"))
  .setOutputCol("answer")
```
The default model is "deberta_v3_xsmall_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 https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see DeBertaForQuestionAnsweringTestSpec.
Example
```
import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val document = new MultiDocumentAssembler()
  .setInputCols("question", "context")
  .setOutputCols("document_question", "document_context")

val questionAnswering = DeBertaForQuestionAnswering.pretrained()
  .setInputCols(Array("document_question", "document_context"))
  .setOutputCol("answer")
  .setCaseSensitive(true)

val pipeline = new Pipeline().setStages(Array(
  document,
  questionAnswering
))

val data = Seq("What's my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(false)
+---------------------+
|result               |
+---------------------+
|[Clara]              |
++--------------------+
```
See also
DeBertaForSequenceClassification for sequence-level classification
Annotators Main Page for a list of transformer based classifiers
class DeBertaForSequenceClassification extends AnnotatorModel[DeBertaForSequenceClassification] with HasBatchedAnnotate[DeBertaForSequenceClassification] with WriteTensorflowModel with WriteSentencePieceModel with HasCaseSensitiveProperties with HasClassifierActivationProperties
DeBertaForSequenceClassification can load DeBerta v2 & v3 Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g.
DeBertaForSequenceClassification can load DeBerta v2 & v3 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:
```
val sequenceClassifier = DeBertaForSequenceClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")
```
The default model is "deberta_v3_xsmall_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 https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see DeBertaForSequenceClassification.
Example
```
import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val tokenizer = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")

val sequenceClassifier = DeBertaForSequenceClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")
  .setCaseSensitive(true)

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  tokenizer,
  sequenceClassifier
))

val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(false)
+--------------------+
|result              |
+--------------------+
|[neg, neg]          |
|[pos, pos, pos, pos]|
+--------------------+
```
See also
DeBertaForSequenceClassification for sequence-level classification
Annotators Main Page for a list of transformer based classifiers

class DeBertaForTokenClassification extends AnnotatorModel[DeBertaForTokenClassification] with HasBatchedAnnotate[DeBertaForTokenClassification] with WriteTensorflowModel with WriteSentencePieceModel with HasCaseSensitiveProperties

DeBertaForTokenClassification can load DeBERTA Models v2 and v3 with a token classification head on top (a linear layer on top of the hidden-states output) e.g.

DeBertaForTokenClassification can load DeBERTA Models v2 and v3 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 pretrained of the companion object:

val tokenClassifier = DeBertaForTokenClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")

The default model is "deberta_v3_xsmall_token_classifier_conll03", if no name is provided.

For available pretrained models please see the Models Hub.

and the DeBertaForTokenClassificationTestSpec. Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. The Spark NLP Workshop example shows how to import them https://github.com/JohnSnowLabs/spark-nlp/discussions/5669.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val tokenizer = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")

val tokenClassifier = DeBertaForTokenClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")
  .setCaseSensitive(true)

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  tokenizer,
  tokenClassifier
))

val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(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]|
+------------------------------------------------------------------------------------+

See also: DeBertaForTokenClassification for token-level classification
Annotators Main Page for a list of transformer based classifiers

class DistilBertForQuestionAnswering extends AnnotatorModel[DistilBertForQuestionAnswering] with HasBatchedAnnotate[DistilBertForQuestionAnswering] with WriteTensorflowModel with HasCaseSensitiveProperties
DistilBertForQuestionAnswering can load DistilBert 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).
DistilBertForQuestionAnswering can load DistilBert 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:
```
val spanClassifier = DistilBertForQuestionAnswering.pretrained()
  .setInputCols(Array("document_question", "document_context"))
  .setOutputCol("answer")
```
The default model is "distilbert_base_cased_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 https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see DistilBertForSequenceClassificationTestSpec.
Example
```
import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val document = new MultiDocumentAssembler()
  .setInputCols("question", "context")
  .setOutputCols("document_question", "document_context")

val questionAnswering = DistilBertForQuestionAnswering.pretrained()
  .setInputCols(Array("document_question", "document_context"))
  .setOutputCol("answer")
  .setCaseSensitive(true)

val pipeline = new Pipeline().setStages(Array(
  document,
  questionAnswering
))

val data = Seq("What's my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(false)
+---------------------+
|result               |
+---------------------+
|[Clara]              |
++--------------------+
```
See also
DistilBertForSequenceClassification for sequence-level classification
Annotators Main Page for a list of transformer based classifiers
class DistilBertForSequenceClassification extends AnnotatorModel[DistilBertForSequenceClassification] with HasBatchedAnnotate[DistilBertForSequenceClassification] with WriteTensorflowModel with HasCaseSensitiveProperties with HasClassifierActivationProperties
DistilBertForSequenceClassification can load DistilBERT Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g.
DistilBertForSequenceClassification can load DistilBERT 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:
```
val sequenceClassifier = DistilBertForSequenceClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")
```
The default model is "distilbert_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 https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see DistilBertForSequenceClassificationTestSpec.
Example
```
import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val tokenizer = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")

val sequenceClassifier = DistilBertForSequenceClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")
  .setCaseSensitive(true)

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  tokenizer,
  sequenceClassifier
))

val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(false)
+--------------------+
|result              |
+--------------------+
|[neg, neg]          |
|[pos, pos, pos, pos]|
+--------------------+
```
See also
DistilBertForSequenceClassification for sequence-level classification
Annotators Main Page for a list of transformer based classifiers

class DistilBertForTokenClassification extends AnnotatorModel[DistilBertForTokenClassification] with HasBatchedAnnotate[DistilBertForTokenClassification] with WriteTensorflowModel with HasCaseSensitiveProperties

DistilBertForTokenClassification can load Bert Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g.

DistilBertForTokenClassification can load Bert 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 pretrained of the companion object:

val tokenClassifier = DistilBertForTokenClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")

The default model is "distilbert_base_token_classifier_conll03", 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 https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see DistilBertForTokenClassificationTestSpec.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val tokenizer = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")

val tokenClassifier = DistilBertForTokenClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")
  .setCaseSensitive(true)

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  tokenizer,
  tokenClassifier
))

val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(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]|
+------------------------------------------------------------------------------------+

See also: DistilBertForTokenClassification for token-level classification
Annotators Main Page for a list of transformer based classifiers

class LongformerForQuestionAnswering extends AnnotatorModel[LongformerForQuestionAnswering] with HasBatchedAnnotate[LongformerForQuestionAnswering] with WriteTensorflowModel with HasCaseSensitiveProperties
LongformerForQuestionAnswering can load Longformer 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).
LongformerForQuestionAnswering can load Longformer 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:
```
val spanClassifier = LongformerForQuestionAnswering.pretrained()
  .setInputCols(Array("document_question", "document_context"))
  .setOutputCol("answer")
```
The default model is "longformer_base_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 https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see LongformerForQuestionAnsweringTestSpec.
Example
```
import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val document = new MultiDocumentAssembler()
  .setInputCols("question", "context")
  .setOutputCols("document_question", "document_context")

val questionAnswering = LongformerForQuestionAnswering.pretrained()
  .setInputCols(Array("document_question", "document_context"))
  .setOutputCol("answer")
  .setCaseSensitive(true)

val pipeline = new Pipeline().setStages(Array(
  document,
  questionAnswering
))

val data = Seq("What's my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(false)
+---------------------+
|result               |
+---------------------+
|[Clara]              |
++--------------------+
```
See also
LongformerForSequenceClassification for sequence-level classification
Annotators Main Page for a list of transformer based classifiers
class LongformerForSequenceClassification extends AnnotatorModel[LongformerForSequenceClassification] with HasBatchedAnnotate[LongformerForSequenceClassification] with WriteTensorflowModel with HasCaseSensitiveProperties with HasClassifierActivationProperties
LongformerForSequenceClassification can load Longformer Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g.
LongformerForSequenceClassification can load Longformer 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:
```
val sequenceClassifier = LongformerForSequenceClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")
```
The default model is "longformer_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 https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see LongformerForSequenceClassification.
Example
```
import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val tokenizer = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")

val sequenceClassifier = LongformerForSequenceClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")
  .setCaseSensitive(true)

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  tokenizer,
  sequenceClassifier
))

val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(false)
+--------------------+
|result              |
+--------------------+
|[neg, neg]          |
|[pos, pos, pos, pos]|
+--------------------+
```
See also
LongformerForSequenceClassification for sequence-level classification
Annotators Main Page for a list of transformer based classifiers

class LongformerForTokenClassification extends AnnotatorModel[LongformerForTokenClassification] with HasBatchedAnnotate[LongformerForTokenClassification] with WriteTensorflowModel with HasCaseSensitiveProperties

LongformerForTokenClassification can load Longformer Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g.

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 pretrained of the companion object:

val tokenClassifier = LongformerForTokenClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")

The default model is "longformer_base_token_classifier_conll03", if no name is provided.

For available pretrained models please see the Models Hub.

and the LongformerForTokenClassificationTestSpec. To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val tokenizer = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")

val tokenClassifier = LongformerForTokenClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")
  .setCaseSensitive(true)

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  tokenizer,
  tokenClassifier
))

val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(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]|
+------------------------------------------------------------------------------------+

See also: LongformerForTokenClassification for token-level classification
Annotators Main Page for a list of transformer based classifiers

class MultiClassifierDLApproach extends AnnotatorApproach[MultiClassifierDLModel] with ParamsAndFeaturesWritable

Trains a MultiClassifierDL for Multi-label Text Classification.

MultiClassifierDL uses a Bidirectional GRU with a convolutional model that we have built inside TensorFlow and supports up to 100 classes.

For instantiated/pretrained models, see MultiClassifierDLModel.

The input to MultiClassifierDL are Sentence Embeddings such as the state-of-the-art UniversalSentenceEncoder, BertSentenceEmbeddings, or SentenceEmbeddings.

In machine learning, multi-label classification and the strongly related problem of multi-output classification are variants of the classification problem where multiple labels may be assigned to each instance. Multi-label classification is a generalization of multiclass classification, which is the single-label problem of categorizing instances into precisely one of more than two classes; in the multi-label problem there is no constraint on how many of the classes the instance can be assigned to. Formally, multi-label classification is the problem of finding a model that maps inputs x to binary vectors y (assigning a value of 0 or 1 for each element (label) in y).

Notes:

This annotator requires an array of labels in type of String.
UniversalSentenceEncoder, BertSentenceEmbeddings, or SentenceEmbeddings can be used for the inputCol.

For extended examples of usage, see the Spark NLP Workshop and the MultiClassifierDLTestSpec.

Example

In this example, the training data has the form (Note: labels can be arbitrary)

mr,ref
"name[Alimentum], area[city centre], familyFriendly[no], near[Burger King]",Alimentum is an adult establish found in the city centre area near Burger King.
"name[Alimentum], area[city centre], familyFriendly[yes]",Alimentum is a family-friendly place in the city centre.
...

It needs some pre-processing first, so the labels are of type Array[String]. This can be done like so:

import spark.implicits._
import com.johnsnowlabs.nlp.annotators.classifier.dl.MultiClassifierDLApproach
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.embeddings.UniversalSentenceEncoder
import org.apache.spark.ml.Pipeline
import org.apache.spark.sql.functions.{col, udf}

// Process training data to create text with associated array of labels
def splitAndTrim = udf { labels: String =>
  labels.split(", ").map(x=>x.trim)
}

val smallCorpus = spark.read
  .option("header", true)
  .option("inferSchema", true)
  .option("mode", "DROPMALFORMED")
  .csv("src/test/resources/classifier/e2e.csv")
  .withColumn("labels", splitAndTrim(col("mr")))
  .withColumn("text", col("ref"))
  .drop("mr")

smallCorpus.printSchema()
// root
// |-- ref: string (nullable = true)
// |-- labels: array (nullable = true)
// |    |-- element: string (containsNull = true)

// Then create pipeline for training
val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")
  .setCleanupMode("shrink")

val embeddings = UniversalSentenceEncoder.pretrained()
  .setInputCols("document")
  .setOutputCol("embeddings")

val docClassifier = new MultiClassifierDLApproach()
  .setInputCols("embeddings")
  .setOutputCol("category")
  .setLabelColumn("labels")
  .setBatchSize(128)
  .setMaxEpochs(10)
  .setLr(1e-3f)
  .setThreshold(0.5f)
  .setValidationSplit(0.1f)

val pipeline = new Pipeline()
  .setStages(
    Array(
      documentAssembler,
      embeddings,
      docClassifier
    )
  )

val pipelineModel = pipeline.fit(smallCorpus)

See also: Multi-label classification on Wikipedia
ClassifierDLApproach for single-class classification
SentimentDLApproach for sentiment analysis

class MultiClassifierDLModel extends AnnotatorModel[MultiClassifierDLModel] with HasSimpleAnnotate[MultiClassifierDLModel] with WriteTensorflowModel with HasStorageRef with ParamsAndFeaturesWritable
MultiClassifierDL for Multi-label Text Classification.
MultiClassifierDL for Multi-label Text Classification.
MultiClassifierDL Bidirectional GRU with Convolution model we have built inside TensorFlow and supports up to 100 classes. The input to MultiClassifierDL is Sentence Embeddings such as state-of-the-art UniversalSentenceEncoder, BertSentenceEmbeddings, or SentenceEmbeddings.
This is the instantiated model of the MultiClassifierDLApproach. For training your own model, please see the documentation of that class.
Pretrained models can be loaded with pretrained of the companion object:
```
val multiClassifier = MultiClassifierDLModel.pretrained()
  .setInputCols("sentence_embeddings")
  .setOutputCol("categories")
```
The default model is "multiclassifierdl_use_toxic", if no name is provided. It uses embeddings from the UniversalSentenceEncoder and classifies toxic comments. The data is based on the Jigsaw Toxic Comment Classification Challenge. For available pretrained models please see the Models Hub.
In machine learning, multi-label classification and the strongly related problem of multi-output classification are variants of the classification problem where multiple labels may be assigned to each instance. Multi-label classification is a generalization of multiclass classification, which is the single-label problem of categorizing instances into precisely one of more than two classes; in the multi-label problem there is no constraint on how many of the classes the instance can be assigned to. Formally, multi-label classification is the problem of finding a model that maps inputs x to binary vectors y (assigning a value of 0 or 1 for each element (label) in y).
For extended examples of usage, see the Spark NLP Workshop and the MultiClassifierDLTestSpec.
Example
```
import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotators.classifier.dl.MultiClassifierDLModel
import com.johnsnowlabs.nlp.embeddings.UniversalSentenceEncoder
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val useEmbeddings = UniversalSentenceEncoder.pretrained()
  .setInputCols("document")
  .setOutputCol("sentence_embeddings")

val multiClassifierDl = MultiClassifierDLModel.pretrained()
  .setInputCols("sentence_embeddings")
  .setOutputCol("classifications")

val pipeline = new Pipeline()
  .setStages(Array(
    documentAssembler,
    useEmbeddings,
    multiClassifierDl
  ))

val data = Seq(
  "This is pretty good stuff!",
  "Wtf kind of crap is this"
).toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("text", "classifications.result").show(false)
+--------------------------+----------------+
|text                      |result          |
+--------------------------+----------------+
|This is pretty good stuff!|[]              |
|Wtf kind of crap is this  |[toxic, obscene]|
+--------------------------+----------------+
```
See also
Multi-label classification on Wikipedia
ClassifierDLModel for single-class classification
SentimentDLModel for sentiment analysis
trait ReadAlbertForQATensorflowModel extends ReadTensorflowModel with ReadSentencePieceModel
trait ReadAlbertForSequenceTensorflowModel extends ReadTensorflowModel with ReadSentencePieceModel
trait ReadAlbertForTokenTensorflowModel extends ReadTensorflowModel with ReadSentencePieceModel
trait ReadBertForQATensorflowModel extends ReadTensorflowModel
trait ReadBertForSequenceTensorflowModel extends ReadTensorflowModel
trait ReadBertForTokenTensorflowModel extends ReadTensorflowModel
trait ReadClassifierDLTensorflowModel extends ReadTensorflowModel
trait ReadDeBertaForQATensorflowModel extends ReadTensorflowModel with ReadSentencePieceModel
trait ReadDeBertaForSequenceTensorflowModel extends ReadTensorflowModel with ReadSentencePieceModel
trait ReadDeBertaForTokenTensorflowModel extends ReadTensorflowModel with ReadSentencePieceModel
trait ReadDistilBertForQATensorflowModel extends ReadTensorflowModel
trait ReadDistilBertForSequenceTensorflowModel extends ReadTensorflowModel
trait ReadDistilBertForTokenTensorflowModel extends ReadTensorflowModel
trait ReadLongformerForQATensorflowModel extends ReadTensorflowModel
trait ReadLongformerForSequenceTensorflowModel extends ReadTensorflowModel
trait ReadLongformerForTokenTensorflowModel extends ReadTensorflowModel
trait ReadMultiClassifierDLTensorflowModel extends ReadTensorflowModel
trait ReadRoBertaForQATensorflowModel extends ReadTensorflowModel
trait ReadRoBertaForSequenceTensorflowModel extends ReadTensorflowModel
trait ReadRoBertaForTokenTensorflowModel extends ReadTensorflowModel
trait ReadSentimentDLTensorflowModel extends ReadTensorflowModel
trait ReadXlmRoBertaForQATensorflowModel extends ReadTensorflowModel with ReadSentencePieceModel
trait ReadXlmRoBertaForSequenceTensorflowModel extends ReadTensorflowModel with ReadSentencePieceModel
trait ReadXlmRoBertaForTokenTensorflowModel extends ReadTensorflowModel with ReadSentencePieceModel
trait ReadXlnetForSequenceTensorflowModel extends ReadTensorflowModel with ReadSentencePieceModel
trait ReadXlnetForTokenTensorflowModel extends ReadTensorflowModel with ReadSentencePieceModel
trait ReadablePretrainedAlbertForQAModel extends ParamsAndFeaturesReadable[AlbertForQuestionAnswering] with HasPretrained[AlbertForQuestionAnswering]
trait ReadablePretrainedAlbertForSequenceModel extends ParamsAndFeaturesReadable[AlbertForSequenceClassification] with HasPretrained[AlbertForSequenceClassification]
trait ReadablePretrainedAlbertForTokenModel extends ParamsAndFeaturesReadable[AlbertForTokenClassification] with HasPretrained[AlbertForTokenClassification]
trait ReadablePretrainedBertForQAModel extends ParamsAndFeaturesReadable[BertForQuestionAnswering] with HasPretrained[BertForQuestionAnswering]
trait ReadablePretrainedBertForSequenceModel extends ParamsAndFeaturesReadable[BertForSequenceClassification] with HasPretrained[BertForSequenceClassification]
trait ReadablePretrainedBertForTokenModel extends ParamsAndFeaturesReadable[BertForTokenClassification] with HasPretrained[BertForTokenClassification]
trait ReadablePretrainedClassifierDL extends ParamsAndFeaturesReadable[ClassifierDLModel] with HasPretrained[ClassifierDLModel]
trait ReadablePretrainedDeBertaForQAModel extends ParamsAndFeaturesReadable[DeBertaForQuestionAnswering] with HasPretrained[DeBertaForQuestionAnswering]
trait ReadablePretrainedDeBertaForSequenceModel extends ParamsAndFeaturesReadable[DeBertaForSequenceClassification] with HasPretrained[DeBertaForSequenceClassification]
trait ReadablePretrainedDeBertaForTokenModel extends ParamsAndFeaturesReadable[DeBertaForTokenClassification] with HasPretrained[DeBertaForTokenClassification]
trait ReadablePretrainedDistilBertForQAModel extends ParamsAndFeaturesReadable[DistilBertForQuestionAnswering] with HasPretrained[DistilBertForQuestionAnswering]
trait ReadablePretrainedDistilBertForSequenceModel extends ParamsAndFeaturesReadable[DistilBertForSequenceClassification] with HasPretrained[DistilBertForSequenceClassification]
trait ReadablePretrainedDistilBertForTokenModel extends ParamsAndFeaturesReadable[DistilBertForTokenClassification] with HasPretrained[DistilBertForTokenClassification]
trait ReadablePretrainedLongformerForQAModel extends ParamsAndFeaturesReadable[LongformerForQuestionAnswering] with HasPretrained[LongformerForQuestionAnswering]
trait ReadablePretrainedLongformerForSequenceModel extends ParamsAndFeaturesReadable[LongformerForSequenceClassification] with HasPretrained[LongformerForSequenceClassification]
trait ReadablePretrainedLongformerForTokenModel extends ParamsAndFeaturesReadable[LongformerForTokenClassification] with HasPretrained[LongformerForTokenClassification]
trait ReadablePretrainedMultiClassifierDL extends ParamsAndFeaturesReadable[MultiClassifierDLModel] with HasPretrained[MultiClassifierDLModel]
trait ReadablePretrainedRoBertaForQAModel extends ParamsAndFeaturesReadable[RoBertaForQuestionAnswering] with HasPretrained[RoBertaForQuestionAnswering]
trait ReadablePretrainedRoBertaForSequenceModel extends ParamsAndFeaturesReadable[RoBertaForSequenceClassification] with HasPretrained[RoBertaForSequenceClassification]
trait ReadablePretrainedRoBertaForTokenModel extends ParamsAndFeaturesReadable[RoBertaForTokenClassification] with HasPretrained[RoBertaForTokenClassification]
trait ReadablePretrainedSentimentDL extends ParamsAndFeaturesReadable[SentimentDLModel] with HasPretrained[SentimentDLModel]
trait ReadablePretrainedXlmRoBertaForQAModel extends ParamsAndFeaturesReadable[XlmRoBertaForQuestionAnswering] with HasPretrained[XlmRoBertaForQuestionAnswering]
trait ReadablePretrainedXlmRoBertaForSequenceModel extends ParamsAndFeaturesReadable[XlmRoBertaForSequenceClassification] with HasPretrained[XlmRoBertaForSequenceClassification]
trait ReadablePretrainedXlmRoBertaForTokenModel extends ParamsAndFeaturesReadable[XlmRoBertaForTokenClassification] with HasPretrained[XlmRoBertaForTokenClassification]
trait ReadablePretrainedXlnetForSequenceModel extends ParamsAndFeaturesReadable[XlnetForSequenceClassification] with HasPretrained[XlnetForSequenceClassification]
trait ReadablePretrainedXlnetForTokenModel extends ParamsAndFeaturesReadable[XlnetForTokenClassification] with HasPretrained[XlnetForTokenClassification]
class RoBertaForQuestionAnswering extends AnnotatorModel[RoBertaForQuestionAnswering] with HasBatchedAnnotate[RoBertaForQuestionAnswering] with WriteTensorflowModel with HasCaseSensitiveProperties
RoBertaForQuestionAnswering can load 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).
RoBertaForQuestionAnswering can load 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:
```
val spanClassifier = RoBertaForQuestionAnswering.pretrained()
  .setInputCols(Array("document_question", "document_context"))
  .setOutputCol("answer")
```
The default model is "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 https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see RoBertaForQuestionAnsweringTestSpec.
Example
```
import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val document = new MultiDocumentAssembler()
  .setInputCols("question", "context")
  .setOutputCols("document_question", "document_context")

val questionAnswering = RoBertaForQuestionAnswering.pretrained()
  .setInputCols(Array("document_question", "document_context"))
  .setOutputCol("answer")
  .setCaseSensitive(true)

val pipeline = new Pipeline().setStages(Array(
  document,
  questionAnswering
))

val data = Seq("What's my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(false)
+---------------------+
|result               |
+---------------------+
|[Clara]              |
++--------------------+
```
See also
RoBertaForSequenceClassification for sequence-level classification
Annotators Main Page for a list of transformer based classifiers
class RoBertaForSequenceClassification extends AnnotatorModel[RoBertaForSequenceClassification] with HasBatchedAnnotate[RoBertaForSequenceClassification] with WriteTensorflowModel with HasCaseSensitiveProperties with HasClassifierActivationProperties
RoBertaForSequenceClassification can load RoBERTa Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g.
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:
```
val 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 https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see RoBertaForSequenceClassification.
Example
```
import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val tokenizer = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")

val sequenceClassifier = RoBertaForSequenceClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")
  .setCaseSensitive(true)

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  tokenizer,
  sequenceClassifier
))

val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(false)
+--------------------+
|result              |
+--------------------+
|[neg, neg]          |
|[pos, pos, pos, pos]|
+--------------------+
```
See also
RoBertaForSequenceClassification for sequence-level classification
Annotators Main Page for a list of transformer based classifiers

class RoBertaForTokenClassification extends AnnotatorModel[RoBertaForTokenClassification] with HasBatchedAnnotate[RoBertaForTokenClassification] with WriteTensorflowModel with HasCaseSensitiveProperties

RoBertaForTokenClassification can load RoBERTa Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g.

RoBertaForTokenClassification can load RoBERTa 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 pretrained of the companion object:

val tokenClassifier = RoBertaForTokenClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")

The default model is "roberta_base_token_classifier_conll03", if no name is provided.

For available pretrained models please see the Models Hub.

and the RoBertaForTokenClassificationTestSpec. To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val tokenizer = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")

val tokenClassifier = RoBertaForTokenClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")
  .setCaseSensitive(true)

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  tokenizer,
  tokenClassifier
))

val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(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]|
+------------------------------------------------------------------------------------+

See also: RoBertaForTokenClassification for token-level classification
Annotators Main Page for a list of transformer based classifiers

class SentimentDLApproach extends AnnotatorApproach[SentimentDLModel] with ParamsAndFeaturesWritable

Trains a SentimentDL, an annotator for multi-class sentiment analysis.

In natural language processing, sentiment analysis is the task of classifying the affective state or subjective view of a text. A common example is if either a product review or tweet can be interpreted positively or negatively.

For the instantiated/pretrained models, see SentimentDLModel.

Notes:

This annotator accepts a label column of a single item in either type of String, Int, Float, or Double. So positive sentiment can be expressed as either "positive" or 0, negative sentiment as "negative" or 1.
UniversalSentenceEncoder, BertSentenceEmbeddings, or SentenceEmbeddings can be used for the inputCol.

For extended examples of usage, see the Spark NLP Workshop and the SentimentDLTestSpec.

Example

In this example, sentiment.csv is in the form

text,label
This movie is the best movie I have watched ever! In my opinion this movie can win an award.,0
This was a terrible movie! The acting was bad really bad!,1

The model can then be trained with

import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotator.UniversalSentenceEncoder
import com.johnsnowlabs.nlp.annotators.classifier.dl.{SentimentDLApproach, SentimentDLModel}
import org.apache.spark.ml.Pipeline

val smallCorpus = spark.read.option("header", "true").csv("src/test/resources/classifier/sentiment.csv")

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val useEmbeddings = UniversalSentenceEncoder.pretrained()
  .setInputCols("document")
  .setOutputCol("sentence_embeddings")

val docClassifier = new SentimentDLApproach()
  .setInputCols("sentence_embeddings")
  .setOutputCol("sentiment")
  .setLabelColumn("label")
  .setBatchSize(32)
  .setMaxEpochs(1)
  .setLr(5e-3f)
  .setDropout(0.5f)

val pipeline = new Pipeline()
  .setStages(
    Array(
      documentAssembler,
      useEmbeddings,
      docClassifier
    )
  )

val pipelineModel = pipeline.fit(smallCorpus)

See also: ClassifierDLApproach for general single-class classification
MultiClassifierDLApproach for general multi-class classification

class SentimentDLModel extends AnnotatorModel[SentimentDLModel] with HasSimpleAnnotate[SentimentDLModel] with WriteTensorflowModel with HasStorageRef with ParamsAndFeaturesWritable

SentimentDL, an annotator for multi-class sentiment analysis.

This is the instantiated model of the SentimentDLApproach. For training your own model, please see the documentation of that class.

Pretrained models can be loaded with pretrained of the companion object:

val sentiment = SentimentDLModel.pretrained()
  .setInputCols("sentence_embeddings")
  .setOutputCol("sentiment")

The default model is "sentimentdl_use_imdb", if no name is provided. It is english sentiment analysis trained on the IMDB dataset. For available pretrained models please see the Models Hub.

For extended examples of usage, see the Spark NLP Workshop and the SentimentDLTestSpec.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotator.UniversalSentenceEncoder
import com.johnsnowlabs.nlp.annotators.classifier.dl.SentimentDLModel
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val useEmbeddings = UniversalSentenceEncoder.pretrained()
  .setInputCols("document")
  .setOutputCol("sentence_embeddings")

val sentiment = SentimentDLModel.pretrained("sentimentdl_use_twitter")
  .setInputCols("sentence_embeddings")
  .setThreshold(0.7F)
  .setOutputCol("sentiment")

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  useEmbeddings,
  sentiment
))

val data = Seq(
  "Wow, the new video is awesome!",
  "bruh what a damn waste of time"
).toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("text", "sentiment.result").show(false)
+------------------------------+----------+
|text                          |result    |
+------------------------------+----------+
|Wow, the new video is awesome!|[positive]|
|bruh what a damn waste of time|[negative]|
+------------------------------+----------+

See also: ClassifierDLModel for general single-class classification
MultiClassifierDLModel for general multi-class classification

class XlmRoBertaForQuestionAnswering extends AnnotatorModel[XlmRoBertaForQuestionAnswering] with HasBatchedAnnotate[XlmRoBertaForQuestionAnswering] with WriteTensorflowModel with WriteSentencePieceModel with HasCaseSensitiveProperties
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).
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:
```
val spanClassifier = XlmRoBertaForQuestionAnswering.pretrained()
  .setInputCols(Array("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 https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see XlmRoBertaForQuestionAnsweringTestSpec.
Example
```
import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val document = new MultiDocumentAssembler()
  .setInputCols("question", "context")
  .setOutputCols("document_question", "document_context")

val questionAnswering = XlmRoBertaForQuestionAnswering.pretrained()
  .setInputCols(Array("document_question", "document_context"))
  .setOutputCol("answer")
  .setCaseSensitive(true)

val pipeline = new Pipeline().setStages(Array(
  document,
  questionAnswering
))

val data = Seq("What's my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(false)
+---------------------+
|result               |
+---------------------+
|[Clara]              |
++--------------------+
```
See also
XlmRoBertaForSequenceClassification for sequence-level classification
Annotators Main Page for a list of transformer based classifiers
class XlmRoBertaForSequenceClassification extends AnnotatorModel[XlmRoBertaForSequenceClassification] with HasBatchedAnnotate[XlmRoBertaForSequenceClassification] with WriteTensorflowModel with WriteSentencePieceModel with HasCaseSensitiveProperties with HasClassifierActivationProperties
XlmRoBertaForSequenceClassification can load XLM-RoBERTa Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g.
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 pretrained of the companion object:
```
val 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.
To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see XlmRoBertaForSequenceClassification.
Example
```
import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val tokenizer = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")

val sequenceClassifier = XlmRoBertaForSequenceClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")
  .setCaseSensitive(true)

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  tokenizer,
  sequenceClassifier
))

val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(false)
+--------------------+
|result              |
+--------------------+
|[neg, neg]          |
|[pos, pos, pos, pos]|
+--------------------+
```
See also
XlmRoBertaForSequenceClassification for sequence-level classification
Annotators Main Page for a list of transformer based classifiers

class XlmRoBertaForTokenClassification extends AnnotatorModel[XlmRoBertaForTokenClassification] with HasBatchedAnnotate[XlmRoBertaForTokenClassification] with WriteTensorflowModel with WriteSentencePieceModel with HasCaseSensitiveProperties

XlmRoBertaForTokenClassification can load XLM-RoBERTa Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g.

XlmRoBertaForTokenClassification can load XLM-RoBERTa 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 pretrained of the companion object:

val tokenClassifier = XlmRoBertaForTokenClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")

The default model is "xlm_roberta_base_token_classifier_conll03", if no name is provided.

For available pretrained models please see the Models Hub.

and the XlmRoBertaForTokenClassificationTestSpec. To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val tokenizer = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")

val tokenClassifier = XlmRoBertaForTokenClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")
  .setCaseSensitive(true)

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  tokenizer,
  tokenClassifier
))

val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(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]|
+------------------------------------------------------------------------------------+

See also: XlmRoBertaForTokenClassification for token-level classification
Annotators Main Page for a list of transformer based classifiers

class XlnetForSequenceClassification extends AnnotatorModel[XlnetForSequenceClassification] with HasBatchedAnnotate[XlnetForSequenceClassification] with WriteTensorflowModel with WriteSentencePieceModel with HasCaseSensitiveProperties with HasClassifierActivationProperties
XlnetForSequenceClassification can load XLNet Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g.
XlnetForSequenceClassification can load XLNet 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:
```
val sequenceClassifier = XlnetForSequenceClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")
```
The default model is "xlnet_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 https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see XlnetForSequenceClassification.
Example
```
import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val tokenizer = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")

val sequenceClassifier = XlnetForSequenceClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")
  .setCaseSensitive(true)

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  tokenizer,
  sequenceClassifier
))

val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(false)
+--------------------+
|result              |
+--------------------+
|[neg, neg]          |
|[pos, pos, pos, pos]|
+--------------------+
```
See also
XlnetForSequenceClassification for sequence-level classification
Annotators Main Page for a list of transformer based classifiers

class XlnetForTokenClassification extends AnnotatorModel[XlnetForTokenClassification] with HasBatchedAnnotate[XlnetForTokenClassification] with WriteTensorflowModel with WriteSentencePieceModel with HasCaseSensitiveProperties

XlnetForTokenClassification can load XLNet Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g.

XlnetForTokenClassification can load XLNet 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 pretrained of the companion object:

val tokenClassifier = XlnetForTokenClassification.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.

and the XlnetForTokenClassificationTestSpec. To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val tokenizer = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")

val tokenClassifier = XlnetForTokenClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")
  .setCaseSensitive(true)

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  tokenizer,
  tokenClassifier
))

val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(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]|
+------------------------------------------------------------------------------------+

See also: XlnetForTokenClassification for token-level classification
Annotators Main Page for a list of transformer based classifiers

Value Members

object AlbertForQuestionAnswering extends ReadablePretrainedAlbertForQAModel with ReadAlbertForQATensorflowModel with Serializable
This is the companion object of AlbertForQuestionAnswering.
This is the companion object of AlbertForQuestionAnswering. Please refer to that class for the documentation.
object AlbertForSequenceClassification extends ReadablePretrainedAlbertForSequenceModel with ReadAlbertForSequenceTensorflowModel with Serializable
This is the companion object of AlbertForSequenceClassification.
This is the companion object of AlbertForSequenceClassification. Please refer to that class for the documentation.
object AlbertForTokenClassification extends ReadablePretrainedAlbertForTokenModel with ReadAlbertForTokenTensorflowModel with Serializable
This is the companion object of AlbertForTokenClassification.
This is the companion object of AlbertForTokenClassification. Please refer to that class for the documentation.
object BertForQuestionAnswering extends ReadablePretrainedBertForQAModel with ReadBertForQATensorflowModel with Serializable
This is the companion object of BertForQuestionAnswering.
This is the companion object of BertForQuestionAnswering. Please refer to that class for the documentation.
object BertForSequenceClassification extends ReadablePretrainedBertForSequenceModel with ReadBertForSequenceTensorflowModel with Serializable
This is the companion object of BertForSequenceClassification.
This is the companion object of BertForSequenceClassification. Please refer to that class for the documentation.
object BertForTokenClassification extends ReadablePretrainedBertForTokenModel with ReadBertForTokenTensorflowModel with Serializable
This is the companion object of BertForTokenClassification.
This is the companion object of BertForTokenClassification. Please refer to that class for the documentation.
object ClassifierDLApproach extends DefaultParamsReadable[ClassifierDLApproach] with Serializable
This is the companion object of ClassifierDLApproach.
This is the companion object of ClassifierDLApproach. Please refer to that class for the documentation.
object ClassifierDLModel extends ReadablePretrainedClassifierDL with ReadClassifierDLTensorflowModel with Serializable
This is the companion object of ClassifierDLModel.
This is the companion object of ClassifierDLModel. Please refer to that class for the documentation.
object DeBertaForQuestionAnswering extends ReadablePretrainedDeBertaForQAModel with ReadDeBertaForQATensorflowModel with Serializable
This is the companion object of DeBertaForSequenceClassification.
This is the companion object of DeBertaForSequenceClassification. Please refer to that class for the documentation.
object DeBertaForSequenceClassification extends ReadablePretrainedDeBertaForSequenceModel with ReadDeBertaForSequenceTensorflowModel with Serializable
This is the companion object of DeBertaForSequenceClassification.
This is the companion object of DeBertaForSequenceClassification. Please refer to that class for the documentation.
object DeBertaForTokenClassification extends ReadablePretrainedDeBertaForTokenModel with ReadDeBertaForTokenTensorflowModel with Serializable
This is the companion object of DeBertaForTokenClassification.
This is the companion object of DeBertaForTokenClassification. Please refer to that class for the documentation.
object DistilBertForQuestionAnswering extends ReadablePretrainedDistilBertForQAModel with ReadDistilBertForQATensorflowModel with Serializable
This is the companion object of DistilBertForQuestionAnswering.
This is the companion object of DistilBertForQuestionAnswering. Please refer to that class for the documentation.
object DistilBertForSequenceClassification extends ReadablePretrainedDistilBertForSequenceModel with ReadDistilBertForSequenceTensorflowModel with Serializable
This is the companion object of DistilBertForSequenceClassification.
This is the companion object of DistilBertForSequenceClassification. Please refer to that class for the documentation.
object DistilBertForTokenClassification extends ReadablePretrainedDistilBertForTokenModel with ReadDistilBertForTokenTensorflowModel with Serializable
This is the companion object of DistilBertForTokenClassification.
This is the companion object of DistilBertForTokenClassification. Please refer to that class for the documentation.
object LongformerForQuestionAnswering extends ReadablePretrainedLongformerForQAModel with ReadLongformerForQATensorflowModel with Serializable
This is the companion object of LongformerForQuestionAnswering.
This is the companion object of LongformerForQuestionAnswering. Please refer to that class for the documentation.
object LongformerForSequenceClassification extends ReadablePretrainedLongformerForSequenceModel with ReadLongformerForSequenceTensorflowModel with Serializable
This is the companion object of LongformerForSequenceClassification.
This is the companion object of LongformerForSequenceClassification. Please refer to that class for the documentation.
object LongformerForTokenClassification extends ReadablePretrainedLongformerForTokenModel with ReadLongformerForTokenTensorflowModel with Serializable
This is the companion object of LongformerForTokenClassification.
This is the companion object of LongformerForTokenClassification. Please refer to that class for the documentation.
object MultiClassifierDLModel extends ReadablePretrainedMultiClassifierDL with ReadMultiClassifierDLTensorflowModel with Serializable
This is the companion object of MultiClassifierDLModel.
This is the companion object of MultiClassifierDLModel. Please refer to that class for the documentation.
object RoBertaForQuestionAnswering extends ReadablePretrainedRoBertaForQAModel with ReadRoBertaForQATensorflowModel with Serializable
This is the companion object of RoBertaForQuestionAnswering.
This is the companion object of RoBertaForQuestionAnswering. Please refer to that class for the documentation.
object RoBertaForSequenceClassification extends ReadablePretrainedRoBertaForSequenceModel with ReadRoBertaForSequenceTensorflowModel with Serializable
This is the companion object of RoBertaForSequenceClassification.
This is the companion object of RoBertaForSequenceClassification. Please refer to that class for the documentation.
object RoBertaForTokenClassification extends ReadablePretrainedRoBertaForTokenModel with ReadRoBertaForTokenTensorflowModel with Serializable
This is the companion object of RoBertaForTokenClassification.
This is the companion object of RoBertaForTokenClassification. Please refer to that class for the documentation.
object SentimentApproach extends DefaultParamsReadable[SentimentDLApproach]
This is the companion object of SentimentApproach.
This is the companion object of SentimentApproach. Please refer to that class for the documentation.
object SentimentDLModel extends ReadablePretrainedSentimentDL with ReadSentimentDLTensorflowModel with Serializable
This is the companion object of SentimentDLModel.
This is the companion object of SentimentDLModel. Please refer to that class for the documentation.
object XlmRoBertaForQuestionAnswering extends ReadablePretrainedXlmRoBertaForQAModel with ReadXlmRoBertaForQATensorflowModel with Serializable
This is the companion object of XlmRoBertaForQuestionAnswering.
This is the companion object of XlmRoBertaForQuestionAnswering. Please refer to that class for the documentation.
object XlmRoBertaForSequenceClassification extends ReadablePretrainedXlmRoBertaForSequenceModel with ReadXlmRoBertaForSequenceTensorflowModel with Serializable
This is the companion object of XlmRoBertaForSequenceClassification.
This is the companion object of XlmRoBertaForSequenceClassification. Please refer to that class for the documentation.
object XlmRoBertaForTokenClassification extends ReadablePretrainedXlmRoBertaForTokenModel with ReadXlmRoBertaForTokenTensorflowModel with Serializable
This is the companion object of XlmRoBertaForTokenClassification.
This is the companion object of XlmRoBertaForTokenClassification. Please refer to that class for the documentation.
object XlnetForSequenceClassification extends ReadablePretrainedXlnetForSequenceModel with ReadXlnetForSequenceTensorflowModel with Serializable
This is the companion object of XlnetForSequenceClassification.
This is the companion object of XlnetForSequenceClassification. Please refer to that class for the documentation.
object XlnetForTokenClassification extends ReadablePretrainedXlnetForTokenModel with ReadXlnetForTokenTensorflowModel with Serializable
This is the companion object of XlnetForTokenClassification.
This is the companion object of XlnetForTokenClassification. Please refer to that class for the documentation.

Packages

dl

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package dl

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