com.johnsnowlabs.nlp.annotators.embeddings
BertSentenceChunkEmbeddings
Companion object BertSentenceChunkEmbeddings
class BertSentenceChunkEmbeddings extends BertSentenceEmbeddings with HandleExceptionParams with HasSafeBatchAnnotate[BertSentenceEmbeddings] with CheckLicense
BERT Sentence embeddings for chunk annotations which take into account the context of the sentence the chunk appeared in. This is an extension of BertSentenceEmbeddings which combines the embedding of a chunk with the embedding of the surrounding sentence. For each input chunk annotation, it finds the corresponding sentence, computes the BERT sentence embedding of both the chunk and the sentence and averages them. The resulting embeddings are useful in cases, in which one needs a numerical representation of a text chunk which is sensitive to the context it appears in.
This model is a subclass of BertSentenceEmbeddings and shares all parameters with it. It can load any pretrained BertSentenceEmbeddings model. Available models can be found at Models Hub.
Two input columns are required - chunk and sentence.
val embeddings = BertSentenceChunkEmbeddings.pretrained() .setInputCols("sentence", "chunk") .setOutputCol("sentence_chunk_bert_embeddings")
The default model is "sent_small_bert_L2_768"
, if no name is provided.
Sources :
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Example
import spark.implicits._ import com.johnsnowlabs.nlp.base.DocumentAssembler import com.johnsnowlabs.nlp.annotator.SentenceDetector import com.johnsnowlabs.nlp.embeddings.BertSentenceEmbeddings import com.johnsnowlabs.nlp.EmbeddingsFinisher import org.apache.spark.ml.Pipeline val documentAssembler = new DocumentAssembler() .setInputCol("text") .setOutputCol("document") val sentenceDetector = new SentenceDetector() .setInputCols("document") .setOutputCol("sentence") val tokenizer = new Tokenizer() .setInputCols("sentence") .setOutputCol("tokens") val wordEmbeddings = BertEmbeddings .pretrained("biobert_pubmed_base_cased") .setInputCols(Array("sentence", "tokens")) .setOutputCol("word_embeddings") val nerModel = MedicalNerModel .pretrained("ner_clinical_biobert", "en", "clinical/models") .setInputCols(Array("sentence", "tokens", "word_embeddings")) .setOutputCol("ner") val nerConverter = new NerConverter() .setInputCols("sentence", "tokens", "ner") .setOutputCol("ner_chunk") val sentenceChunkEmbeddings = BertSentenceChunkEmbeddings .pretrained("sbluebert_base_uncased_mli", "en", "clinical/models") .setInputCols(Array("sentence", "ner_chunk")) .setOutputCol("sentence_chunk_embeddings") val pipeline = new Pipeline() .setStages(Array( documentAssembler, sentenceDetector, tokenizer, wordEmbeddings, nerModel, nerConverter, sentenceChunkEmbeddings)) val sampleText = "Her Diabetes has become type 2 in the last year with her Diabetes." + " He complains of swelling in his right forearm." val testDataset = Seq("").toDS.toDF("text") val result = pipeline.fit(emptyDataset).transform(testDataset) result .selectExpr("explode(sentence_chunk_embeddings) AS s") .selectExpr("s.result", "slice(s.embeddings, 1, 5) AS averageEmbedding") .show(truncate=false) +-----------------------------+-----------------------------------------------------------------+ | result| averageEmbedding| +-----------------------------+-----------------------------------------------------------------+ |Her Diabetes |[-0.31995273, -0.04710883, -0.28973156, -0.1294758, 0.12481072] | |type 2 |[-0.027161136, -0.24613449, -0.0949309, 0.1825444, -0.2252143] | |her Diabetes |[-0.31995273, -0.04710883, -0.28973156, -0.1294758, 0.12481072] | |swelling in his right forearm|[-0.45139068, 0.12400375, -0.0075617577, -0.90806055, 0.12871636]| +-----------------------------+-----------------------------------------------------------------+
- See also
BertEmbeddings for token-level embeddings
BertSentenceEmbeddings for sentence-level embeddings
Annotators Main Page for a list of transformer based embeddings
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_transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame
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def
afterAnnotate(dataset: DataFrame): DataFrame
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asInstanceOf[T0]: T0
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def
averageEmbeddings(chunkEmbedding: Array[Float], sentenceEmbedding: Array[Float]): Array[Float]
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def
batchAnnotate(batchedAnnotations: Seq[Array[Annotation]]): Seq[Seq[Annotation]]
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- BertSentenceChunkEmbeddings → BertSentenceEmbeddings → HasBatchedAnnotate
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def
batchProcess(rows: Iterator[_]): Iterator[Row]
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val
batchSize: IntParam
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checkValidEnvironment(spark: Option[SparkSession], scopes: Seq[String]): Unit
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def
checkValidScopesAndEnvironment(scopes: Seq[String], spark: Option[SparkSession], checkLp: Boolean): Unit
- Definition Classes
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val
chunkWeight: FloatParam
Relative weight of chunk embeddings in comparison to sentence embeddings.
Relative weight of chunk embeddings in comparison to sentence embeddings. The value should between 0 and 1. The default is 0.5, which means the chunk and sentence embeddings are given equal weight.
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final
def
clear(param: Param[_]): BertSentenceChunkEmbeddings.this.type
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copy(extra: ParamMap): BertSentenceEmbeddings
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dimension: ProtectedParam[Int]
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doExceptionHandling: BooleanParam
If true, exceptions are handled.
If true, exceptions are handled. If exception causing data is passed to the model, a error annotation is emitted which has the exception message. Processing continues with the next one. This comes with a performance penalty.
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getBatchSize: Int
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def
getCaseSensitive: Boolean
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def
getChunkWeight: Float
Get the relative value of the chunk embeddings.
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final
def
getClass(): Class[_]
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def
getConfigProtoBytes: Option[Array[Byte]]
- Definition Classes
- BertSentenceEmbeddings
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final
def
getDefault[T](param: Param[T]): Option[T]
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getDimension: Int
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getEngine: String
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def
getInputCols: Array[String]
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def
getIsLong: Boolean
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- BertSentenceEmbeddings
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def
getLazyAnnotator: Boolean
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def
getMaxSentenceLength: Int
- Definition Classes
- BertSentenceEmbeddings
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def
getModelIfNotSet: Bert
- Definition Classes
- BertSentenceEmbeddings
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final
def
getOrDefault[T](param: Param[T]): T
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final
def
getOutputCol: String
- Definition Classes
- HasOutputAnnotationCol
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def
getParam(paramName: String): Param[Any]
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def
getSignatures: Option[Map[String, String]]
- Definition Classes
- BertSentenceEmbeddings
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def
getStorageRef: String
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final
def
hasDefault[T](param: Param[T]): Boolean
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inExceptionMode: Boolean
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def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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def
initializeLogIfNecessary(isInterpreter: Boolean): Unit
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val
inputAnnotatorTypes: Array[AnnotatorType]
Input annotator types: DOCUMENT,CHUNK
Input annotator types: DOCUMENT,CHUNK
- Definition Classes
- BertSentenceChunkEmbeddings → BertSentenceEmbeddings → HasInputAnnotationCols
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final
val
inputCols: StringArrayParam
- Attributes
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- HasInputAnnotationCols
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isDefined(param: Param[_]): Boolean
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log: Logger
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logInfo(msg: ⇒ String): Unit
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logName: String
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logTrace(msg: ⇒ String, throwable: Throwable): Unit
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def
logTrace(msg: ⇒ String): Unit
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def
logWarning(msg: ⇒ String, throwable: Throwable): Unit
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def
logWarning(msg: ⇒ String): Unit
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- Logging
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val
maxSentenceLength: IntParam
- Definition Classes
- BertSentenceEmbeddings
-
def
msgHelper(schema: StructType): String
- Attributes
- protected
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- HasInputAnnotationCols
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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def
onWrite(path: String, spark: SparkSession): Unit
- Definition Classes
- BertSentenceEmbeddings → ParamsAndFeaturesWritable
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val
optionalInputAnnotatorTypes: Array[String]
- Definition Classes
- HasInputAnnotationCols
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val
outputAnnotatorType: AnnotatorType
Output annotator types: CHUNK
Output annotator types: CHUNK
- Definition Classes
- BertSentenceChunkEmbeddings → BertSentenceEmbeddings → HasOutputAnnotatorType
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final
val
outputCol: Param[String]
- Attributes
- protected
- Definition Classes
- HasOutputAnnotationCol
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lazy val
params: Array[Param[_]]
- Definition Classes
- Params
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var
parent: Estimator[BertSentenceEmbeddings]
- Definition Classes
- Model
-
def
safeBatchAnnotate(batchedAnnotations: Seq[Array[Annotation]]): Seq[Seq[Annotation]]
- Attributes
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- HasSafeBatchAnnotate
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def
save(path: String): Unit
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- MLWritable
- Annotations
- @Since( "1.6.0" ) @throws( ... )
-
def
sentenceEndTokenId: Int
- Definition Classes
- BertSentenceEmbeddings
-
def
sentenceStartTokenId: Int
- Definition Classes
- BertSentenceEmbeddings
-
def
set[T](param: ProtectedParam[T], value: T): BertSentenceChunkEmbeddings.this.type
- Definition Classes
- HasProtectedParams
-
def
set[T](feature: StructFeature[T], value: T): BertSentenceChunkEmbeddings.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
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def
set[K, V](feature: MapFeature[K, V], value: Map[K, V]): BertSentenceChunkEmbeddings.this.type
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- HasFeatures
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def
set[T](feature: SetFeature[T], value: Set[T]): BertSentenceChunkEmbeddings.this.type
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- HasFeatures
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def
set[T](feature: ArrayFeature[T], value: Array[T]): BertSentenceChunkEmbeddings.this.type
- Attributes
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final
def
set(paramPair: ParamPair[_]): BertSentenceChunkEmbeddings.this.type
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final
def
set(param: String, value: Any): BertSentenceChunkEmbeddings.this.type
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final
def
set[T](param: Param[T], value: T): BertSentenceChunkEmbeddings.this.type
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def
setBatchSize(size: Int): BertSentenceChunkEmbeddings.this.type
- Definition Classes
- HasBatchedAnnotate
-
def
setCaseSensitive(value: Boolean): BertSentenceChunkEmbeddings.this.type
- Definition Classes
- BertSentenceEmbeddings → HasCaseSensitiveProperties
-
def
setChunkWeight(value: Float): BertSentenceChunkEmbeddings.this.type
Sets the wieght of the chunk embeddings relative to the sentence embeddings.
Sets the wieght of the chunk embeddings relative to the sentence embeddings. The value should between 0 and 1.
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def
setConfigProtoBytes(bytes: Array[Int]): BertSentenceChunkEmbeddings.this.type
- Definition Classes
- BertSentenceEmbeddings
-
def
setDefault[T](feature: StructFeature[T], value: () ⇒ T): BertSentenceChunkEmbeddings.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): BertSentenceChunkEmbeddings.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): BertSentenceChunkEmbeddings.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): BertSentenceChunkEmbeddings.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
final
def
setDefault(paramPairs: ParamPair[_]*): BertSentenceChunkEmbeddings.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
setDefault[T](param: Param[T], value: T): BertSentenceChunkEmbeddings.this.type
- Attributes
- protected[org.apache.spark.ml]
- Definition Classes
- Params
-
def
setDimension(value: Int): BertSentenceChunkEmbeddings.this.type
- Definition Classes
- BertSentenceEmbeddings → HasEmbeddingsProperties
-
def
setDoExceptionHandling(value: Boolean): BertSentenceChunkEmbeddings.this.type
If true, exceptions are handled.
If true, exceptions are handled. If exception causing data is passed to the model, a error annotation is emitted which has the exception message. Processing continues with the next one. This comes with a performance penalty.
- Definition Classes
- HandleExceptionParams
-
final
def
setInputCols(value: String*): BertSentenceChunkEmbeddings.this.type
- Definition Classes
- HasInputAnnotationCols
-
def
setInputCols(value: Array[String]): BertSentenceChunkEmbeddings.this.type
- Definition Classes
- HasInputAnnotationCols
-
def
setIsLong(value: Boolean): BertSentenceChunkEmbeddings.this.type
- Definition Classes
- BertSentenceEmbeddings
-
def
setLazyAnnotator(value: Boolean): BertSentenceChunkEmbeddings.this.type
- Definition Classes
- CanBeLazy
-
def
setMaxSentenceLength(value: Int): BertSentenceChunkEmbeddings.this.type
- Definition Classes
- BertSentenceEmbeddings
-
def
setModelIfNotSet(spark: SparkSession, tensorflowWrapper: Option[TensorflowWrapper], onnxWrapper: Option[OnnxWrapper], openvinoWrapper: Option[OpenvinoWrapper]): BertSentenceChunkEmbeddings.this.type
- Definition Classes
- BertSentenceEmbeddings
-
final
def
setOutputCol(value: String): BertSentenceChunkEmbeddings.this.type
- Definition Classes
- HasOutputAnnotationCol
-
def
setParent(parent: Estimator[BertSentenceEmbeddings]): BertSentenceEmbeddings
- Definition Classes
- Model
-
def
setSignatures(value: Map[String, String]): BertSentenceChunkEmbeddings.this.type
- Definition Classes
- BertSentenceEmbeddings
-
def
setStorageRef(value: String): BertSentenceChunkEmbeddings.this.type
- Definition Classes
- HasStorageRef
-
def
setVocabulary(value: Map[String, Int]): BertSentenceChunkEmbeddings.this.type
- Definition Classes
- BertSentenceEmbeddings
-
val
signatures: MapFeature[String, String]
- Definition Classes
- BertSentenceEmbeddings
-
val
storageRef: Param[String]
- Definition Classes
- HasStorageRef
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
-
def
tokenize(sentences: Seq[Sentence]): Seq[WordpieceTokenizedSentence]
- Definition Classes
- BertSentenceEmbeddings
-
final
def
transform(dataset: Dataset[_]): DataFrame
- Definition Classes
- AnnotatorModel → Transformer
-
def
transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" )
-
def
transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" ) @varargs()
-
final
def
transformSchema(schema: StructType): StructType
- Definition Classes
- RawAnnotator → PipelineStage
-
def
transformSchema(schema: StructType, logging: Boolean): StructType
- Attributes
- protected
- Definition Classes
- PipelineStage
- Annotations
- @DeveloperApi()
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val
uid: String
- Definition Classes
- BertSentenceChunkEmbeddings → BertSentenceEmbeddings → Identifiable
-
def
validate(schema: StructType): Boolean
- Attributes
- protected
- Definition Classes
- RawAnnotator
-
def
validateStorageRef(dataset: Dataset[_], inputCols: Array[String], annotatorType: String): Unit
- Definition Classes
- HasStorageRef
-
val
vocabulary: MapFeature[String, Int]
- Definition Classes
- BertSentenceEmbeddings
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
-
def
wrapColumnMetadata(col: Column): Column
- Attributes
- protected
- Definition Classes
- RawAnnotator
-
def
wrapEmbeddingsMetadata(col: Column, embeddingsDim: Int, embeddingsRef: Option[String]): Column
- Attributes
- protected
- Definition Classes
- HasEmbeddingsProperties
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def
wrapSentenceEmbeddingsMetadata(col: Column, embeddingsDim: Int, embeddingsRef: Option[String]): Column
- Attributes
- protected
- Definition Classes
- HasEmbeddingsProperties
-
def
write: MLWriter
- Definition Classes
- ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
-
def
writeOnnxModel(path: String, spark: SparkSession, onnxWrapper: OnnxWrapper, suffix: String, fileName: String): Unit
- Definition Classes
- WriteOnnxModel
-
def
writeOnnxModels(path: String, spark: SparkSession, onnxWrappersWithNames: Seq[(OnnxWrapper, String)], suffix: String): Unit
- Definition Classes
- WriteOnnxModel
-
def
writeOpenvinoModel(path: String, spark: SparkSession, openvinoWrapper: OpenvinoWrapper, suffix: String, fileName: String): Unit
- Definition Classes
- WriteOpenvinoModel
-
def
writeOpenvinoModels(path: String, spark: SparkSession, ovWrappersWithNames: Seq[(OpenvinoWrapper, String)], suffix: String): Unit
- Definition Classes
- WriteOpenvinoModel
-
def
writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String): Unit
- Definition Classes
- WriteTensorflowModel
-
def
writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]]): Unit
- Definition Classes
- WriteTensorflowModel
-
def
writeTensorflowModelV2(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]], savedSignatures: Option[Map[String, String]]): Unit
- Definition Classes
- WriteTensorflowModel