trait DisambiguatorModelParams extends Params with HasFeatures
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def
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def
$[T](param: Param[T]): T
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def
$$[T](feature: StructFeature[T]): T
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def
$$[K, V](feature: MapFeature[K, V]): Map[K, V]
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def
$$[T](feature: SetFeature[T]): Set[T]
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def
$$[T](feature: ArrayFeature[T]): Array[T]
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asInstanceOf[T0]: T0
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def
clear(param: Param[_]): DisambiguatorModelParams.this.type
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clone(): AnyRef
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def
copyValues[T <: Params](to: T, extra: ParamMap): T
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def
defaultCopy[T <: Params](extra: ParamMap): T
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val
embeddingTypeParam: Param[String]
Can be 'bow' for word embeddings or 'sentence' for sentences (Default: sentence)
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final
def
eq(arg0: AnyRef): Boolean
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equals(arg0: Any): Boolean
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def
explainParam(param: Param[_]): String
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explainParams(): String
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def
extractParamMap(): ParamMap
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def
extractParamMap(extra: ParamMap): ParamMap
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val
features: ArrayBuffer[Feature[_, _, _]]
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def
finalize(): Unit
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def
get[T](feature: StructFeature[T]): Option[T]
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def
get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
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def
get[T](feature: SetFeature[T]): Option[Set[T]]
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def
get[T](feature: ArrayFeature[T]): Option[Array[T]]
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def
get[T](param: Param[T]): Option[T]
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def
getClass(): Class[_]
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final
def
getDefault[T](param: Param[T]): Option[T]
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def
getEmbeddingType: String
Can be 'bow' for word embeddings or 'sentence' for sentences (Default: sentence)
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def
getLevenshteinDistanceThresholdParam: Double
Levenshtein distance threshold to narrow results from prefix search (Default: 0.1)
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def
getNarrowWithApproximateMatching: Boolean
Whether to narrow prefix search results with levenstein distance based matching (Default: true)
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def
getNearMatchingGapParam: Int
Puts a limit on a string length (by trimming the candidate chunks) during levenshtein-distance based narrowing, len(candidate) - len(entity chunk) > nearMatchingGap (Default: 4).
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def
getNumFirstChars: Int
How many characters should be considered for initial prefix search in knowledge base
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final
def
getOrDefault[T](param: Param[T]): T
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def
getParam(paramName: String): Param[Any]
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def
getPredictionLimit: Int
Limit on amount of predictions N for topN predictions (Default: 100)
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def
getTokenSearch: Boolean
Whether to search by token or by chunk in knowledge base (Default: true)
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final
def
hasDefault[T](param: Param[T]): Boolean
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def
hasParam(paramName: String): Boolean
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def
hashCode(): Int
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def
isDefined(param: Param[_]): Boolean
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final
def
isInstanceOf[T0]: Boolean
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final
def
isSet(param: Param[_]): Boolean
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val
levenshteinDistanceThresholdParam: DoubleParam
Levenshtein distance threshold to narrow results from prefix search (Default: 0.1)
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val
narrowWithApproximateMatching: BooleanParam
Whether to narrow prefix search results with levenstein distance based matching (Default: true)
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final
def
ne(arg0: AnyRef): Boolean
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val
nearMatchingGapParam: IntParam
Puts a limit on a string length (by trimming the candidate chunks) during levenshtein-distance based narrowing, len(candidate) - len(entity chunk) > nearMatchingGap (Default: 4).
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def
notify(): Unit
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def
notifyAll(): Unit
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val
numFirstChars: IntParam
How many characters should be considered for initial prefix search in knowledge base
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lazy val
params: Array[Param[_]]
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- Params
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val
predictionsLimit: IntParam
Limit on amount of predictions N for topN predictions (Default: 100)
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def
set[T](feature: StructFeature[T], value: T): DisambiguatorModelParams.this.type
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- HasFeatures
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def
set[K, V](feature: MapFeature[K, V], value: Map[K, V]): DisambiguatorModelParams.this.type
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- HasFeatures
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def
set[T](feature: SetFeature[T], value: Set[T]): DisambiguatorModelParams.this.type
- Attributes
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- HasFeatures
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def
set[T](feature: ArrayFeature[T], value: Array[T]): DisambiguatorModelParams.this.type
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final
def
set(paramPair: ParamPair[_]): DisambiguatorModelParams.this.type
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final
def
set(param: String, value: Any): DisambiguatorModelParams.this.type
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final
def
set[T](param: Param[T], value: T): DisambiguatorModelParams.this.type
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def
setDefault[T](feature: StructFeature[T], value: () ⇒ T): DisambiguatorModelParams.this.type
- Attributes
- protected
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- HasFeatures
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def
setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): DisambiguatorModelParams.this.type
- Attributes
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- HasFeatures
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def
setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): DisambiguatorModelParams.this.type
- Attributes
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def
setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): DisambiguatorModelParams.this.type
- Attributes
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final
def
setDefault(paramPairs: ParamPair[_]*): DisambiguatorModelParams.this.type
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final
def
setDefault[T](param: Param[T], value: T): DisambiguatorModelParams.this.type
- Attributes
- protected[org.apache.spark.ml]
- Definition Classes
- Params
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def
setEmbeddingType(v: String): DisambiguatorModelParams.this.type
Can be 'bow' for word embeddings or 'sentence' for sentences (Default: sentence)
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def
setLevenshteinDistanceThresholdParam(v: Double): DisambiguatorModelParams.this.type
Levenshtein distance threshold to narrow results from prefix search (Default: 0.1)
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def
setNarrowWithApproximateMatching(v: Boolean): DisambiguatorModelParams.this.type
Whether to narrow prefix search results with levenstein distance based matching (Default: true)
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def
setNearMatchingGapParam(v: Int): DisambiguatorModelParams.this.type
Puts a limit on a string length (by trimming the candidate chunks) during levenshtein-distance based narrowing, len(candidate) - len(entity chunk) > nearMatchingGap (Default: 4).
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def
setNumFirstChars(v: Int): DisambiguatorModelParams.this.type
How many characters should be considered for initial prefix search in knowledge base
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def
setPredictionLimit(v: Int): DisambiguatorModelParams.this.type
Limit on amount of predictions N for topN predictions (Default: 100)
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def
setTokenSearch(v: Boolean): DisambiguatorModelParams.this.type
Whether to search by token or by chunk in knowledge base (Default: true)
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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val
tokenSearch: BooleanParam
Whether to search by token or by chunk in knowledge base (Default: true)
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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