commonMain.com.xebia.functional.openai.generated.model.FineTuningJobCheckpointMetrics.kt Maven / Gradle / Ivy

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Building applications with LLMs through composability in Kotlin

There is a newer version: 0.0.5-alpha.118

/**
 *
 * Please note:
 * This class is auto generated by OpenAPI Generator (https://openapi-generator.tech).
 * Do not edit this file manually.
 *
 */

@file:Suppress(
    "ArrayInDataClass",
    "EnumEntryName",
    "RemoveRedundantQualifierName",
    "UnusedImport"
)

package com.xebia.functional.openai.generated.model




import kotlinx.serialization.Serializable
import kotlinx.serialization.SerialName
import kotlinx.serialization.Contextual
import kotlin.js.JsName
import kotlinx.serialization.json.*

/**
* Metrics at the step number during the fine-tuning job.
*
    * @param step 
    * @param trainLoss 
    * @param trainMeanTokenAccuracy 
    * @param validLoss 
    * @param validMeanTokenAccuracy 
    * @param fullValidLoss 
    * @param fullValidMeanTokenAccuracy 
*/
@Serializable


data class FineTuningJobCheckpointMetrics (
        @SerialName(value = "step") val step: kotlin.Double? = null,
        @SerialName(value = "train_loss") val trainLoss: kotlin.Double? = null,
        @SerialName(value = "train_mean_token_accuracy") val trainMeanTokenAccuracy: kotlin.Double? = null,
        @SerialName(value = "valid_loss") val validLoss: kotlin.Double? = null,
        @SerialName(value = "valid_mean_token_accuracy") val validMeanTokenAccuracy: kotlin.Double? = null,
        @SerialName(value = "full_valid_loss") val fullValidLoss: kotlin.Double? = null,
        @SerialName(value = "full_valid_mean_token_accuracy") val fullValidMeanTokenAccuracy: kotlin.Double? = null
) {

}