class Gliner2ExtractionResult extends AnyRef
Complete extraction result for one sample.
Contains all extracted information across all task types.
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- new Gliner2ExtractionResult()
Value Members
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final
def
!=(arg0: Any): Boolean
- Definition Classes
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final
def
##(): Int
- Definition Classes
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final
def
==(arg0: Any): Boolean
- Definition Classes
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- def addClassification(taskName: String, label: String, score: Float): Unit
- def addEntities(label: String, spans: Array[Gliner2SpanResult]): Unit
- def addRelations(label: String, spans: Array[Gliner2RelationResult]): Unit
- def addStructure(name: String, value: Array[Map[String, String]]): Unit
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final
def
asInstanceOf[T0]: T0
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- val classifications: Map[String, Array[Gliner2Classification]]
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def
clone(): AnyRef
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- protected[lang]
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- @throws( ... ) @native()
- val entities: Map[String, Array[Gliner2SpanResult]]
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
finalize(): Unit
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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def
isEmpty: Boolean
Check if result is empty (no extractions).
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final
def
isInstanceOf[T0]: Boolean
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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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- val relations: Map[String, Array[Gliner2RelationResult]]
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val
structures: Map[String, Array[Map[String, String]]]
Extracted structure instances per structure name.
Extracted structure instances per structure name.
Each instance is a Map from field name to a JSON-encoded value string, compatible with Spark's MapType(StringType, StringType). Field values encode:
- Span list:
[{"text":"...","confidence":0.9,"start":0,"end":5},...] - Single span:
{"text":"...","confidence":0.9,"start":0,"end":5} - Classification list:
[{"text":"...","confidence":0.9},...] - Single classification:
{"text":"...","confidence":0.9}
Fields with no match are omitted from the map.
- Span list:
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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def
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def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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