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package com.johnsnowlabs.nlp
import org.apache.spark.ml.param._
/** Parameters to configure beam search text generation. */
trait HasGeneratorProperties {
this: ParamsAndFeaturesWritable =>
/** Set transformer task, e.g. `"summarize:"` (Default: `""`).
*
* @group param
*/
val task = new Param[String](this, "task", "Set transformer task, e.g. 'summarize'")
/** @group setParam */
def setTask(value: String): this.type = {
if (get(task).isEmpty)
set(task, value)
this
}
/** @group getParam */
def getTask: Option[String] = get(this.task)
/** max length of the input sequence (Default: `0`)
*
* @group param
*/
val maxInputLength =
new IntParam(this, "maxInputLength", "Maximum length of the input sequence")
def setMaxInputLength(value: Int): this.type = {
set(maxInputLength, value)
this
}
/** Minimum length of the sequence to be generated (Default: `0`)
*
* @group param
*/
val minOutputLength =
new IntParam(this, "minOutputLength", "Minimum length of the sequence to be generated")
/** @group setParam */
def setMinOutputLength(value: Int): this.type = {
set(minOutputLength, value)
this
}
/** @group getParam */
def getMinOutputLength: Int = $(this.minOutputLength)
/** Maximum length of the sequence to be generated (Default: `20`)
*
* @group param
*/
val maxOutputLength =
new IntParam(this, "maxOutputLength", "Maximum length of the sequence to be generated")
/** @group setParam */
def setMaxOutputLength(value: Int): this.type = {
set(maxOutputLength, value)
this
}
/** @group getParam */
def getMaxOutputLength: Int = $(this.maxOutputLength)
/** Whether or not to use sampling, use greedy decoding otherwise (Default: `false`)
*
* @group param
*/
val doSample = new BooleanParam(
this,
"doSample",
"Whether or not to use sampling; use greedy decoding otherwise")
/** @group setParam */
def setDoSample(value: Boolean): this.type = {
set(doSample, value)
this
}
/** @group getParam */
def getDoSample: Boolean = $(this.doSample)
/** The value used to module the next token probabilities (Default: `1.0`)
*
* @group param
*/
val temperature =
new DoubleParam(this, "temperature", "The value used to module the next token probabilities")
/** @group setParam */
def setTemperature(value: Double): this.type = {
set(temperature, value)
this
}
/** @group getParam */
def getTemperature: Double = $(this.temperature)
/** The number of highest probability vocabulary tokens to keep for top-k-filtering (Default:
* `50`)
*
* @group param
*/
val topK = new IntParam(
this,
"topK",
"The number of highest probability vocabulary tokens to keep for top-k-filtering")
/** @group setParam */
def setTopK(value: Int): this.type = {
set(topK, value)
this
}
/** @group getParam */
def getTopK: Int = $(this.topK)
/** If set to float < `1.0`, only the most probable tokens with probabilities that add up to
* `topP` or higher are kept for generation (Default: `1.0`)
*
* @group param
*/
val topP = new DoubleParam(
this,
"topP",
"If set to float < 1, only the most probable tokens with probabilities that add up to ``top_p`` or higher are kept for generation")
/** @group setParam */
def setTopP(value: Double): this.type = {
set(topP, value)
this
}
/** @group getParam */
def getTopP: Double = $(this.topP)
/** The parameter for repetition penalty (Default: `1.0`). `1.0` means no penalty. See
* [[https://arxiv.org/pdf/1909.05858.pdf this paper]] for more details.
*
* @group param
*/
val repetitionPenalty = new DoubleParam(
this,
"repetitionPenalty",
"The parameter for repetition penalty. 1.0 means no penalty.")
/** @group setParam */
def setRepetitionPenalty(value: Double): this.type = {
set(repetitionPenalty, value)
this
}
/** @group getParam */
def getRepetitionPenalty: Double = $(this.repetitionPenalty)
/** If set to int > `0`, all ngrams of that size can only occur once (Default: `0`)
*
* @group param
*/
val noRepeatNgramSize = new IntParam(
this,
"noRepeatNgramSize",
"If set to int > 0, all ngrams of that size can only occur once")
/** @group setParam */
def setNoRepeatNgramSize(value: Int): this.type = {
set(noRepeatNgramSize, value)
this
}
/** @group getParam */
def getNoRepeatNgramSize: Int = $(this.noRepeatNgramSize)
/** Optional Random seed for the model. Needs to be of type `Int`.
*
* @group param
*/
var randomSeed: Option[Long] = None
/** @group setParam */
def setRandomSeed(value: Long): this.type = {
if (randomSeed.isEmpty) {
this.randomSeed = Some(value)
}
this
}
/** @group getParam */
def getRandomSeed: Option[Long] = this.randomSeed
/** Beam size for the beam search algorithm (Default: `4`)
*
* @group param
*/
val beamSize = new IntParam(this, "beamSize", "Number of beams for beam search.")
/** @group setParam */
def setBeamSize(beamNum: Int): this.type = {
set(beamSize, beamNum)
}
/** @group getParam */
def getBeamSize: Int = $(beamSize)
/** The number of sequences to return from the beam search.
*
* @group param
*/
val nReturnSequences = new IntParam(
this,
"nReturnSequences",
"The number of sequences to return from the beam search.")
/** @group setParam */
def setNReturnSequences(beamNum: Int): this.type = {
set(nReturnSequences, beamNum)
}
/** @group getParam */
def getNReturnSequences: Int = $(nReturnSequences)
/** Stop tokens to terminate the generation
*
* @group param
*/
val stopTokenIds =
new IntArrayParam(this, "stopTokenIds", "Stop tokens to terminate the generation")
/** @group setParam */
def setStopTokenIds(value: Array[Int]): this.type = {
set(stopTokenIds, value)
}
/** @group getParam */
def getStopTokenIds: Array[Int] = $(stopTokenIds)
}
