Type Members

1.  case class DocumentSimilarityRankerFinisher(uid: String) extends Transformer with DefaultParamsWritable with Product with Serializable
2.  case class GGUFRankingFinisher(uid: String) extends Transformer with DefaultParamsWritable with Product with Serializable

   Finisher for AutoGGUFReranker outputs that provides ranking capabilities including top-k selection, sorting by relevance score, and score normalization.

   Finisher for AutoGGUFReranker outputs that provides ranking capabilities including top-k selection, sorting by relevance score, and score normalization.

   This finisher processes the output of AutoGGUFReranker, which contains documents with relevance scores in their metadata. It provides several options for post-processing:

   • Top-k selection: Select only the top k documents by relevance score
   • Score thresholding: Filter documents by minimum relevance score
   • Min-max scaling: Normalize relevance scores to 0-1 range
   • Sorting: Sort documents by relevance score in descending order
   • Ranking: Add rank information to document metadata

   The finisher preserves the document annotation structure while adding ranking information to the metadata and optionally filtering/sorting the documents.

   Example

   import com.johnsnowlabs.nlp.base._
   import com.johnsnowlabs.nlp.annotators._
   import com.johnsnowlabs.nlp.finisher._
   import org.apache.spark.ml.Pipeline
   import spark.implicits._
   
   val document = new DocumentAssembler()
     .setInputCol("text")
     .setOutputCol("document")
   
   val reranker = AutoGGUFReranker
     .pretrained("bge_reranker_v2_m3-Q4_K_M")
     .setInputCols("document")
     .setOutputCol("reranked_documents")
     .setQuery("A man is eating pasta.")
   
   val finisher = new GGUFRankingFinisher()
     .setInputCols("reranked_documents")
     .setOutputCol("ranked_documents")
     .setTopK(3)
     .setMinRelevanceScore(0.1)
     .setMinMaxScaling(true)
   
   val pipeline = new Pipeline().setStages(Array(document, reranker, finisher))
   
   val data = Seq(
     "A man is eating food.",
     "A man is eating a piece of bread.",
     "The girl is carrying a baby.",
     "A man is riding a horse."
   ).toDF("text")
   
   val result = pipeline.fit(data).transform(data)
   result.select("ranked_documents").show(truncate = false)
   // Documents will be sorted by relevance with rank information in metadata

   uid

   required uid for storing finisher to disk