final case class Hyperparams(batchSize: Int, classificationNClasses: Optional[Int] = Optional.Absent, classificationPositiveClass: Optional[String] = Optional.Absent, computeClassificationMetrics: Optional[Boolean] = Optional.Absent, learningRateMultiplier: Double, nEpochs: Int, promptLossWeight: Double) extends Product with Serializable
hyperparams model
The hyperparameters used for the fine-tuning job. See the [fine-tuning guide](/docs/guides/legacy-fine-tuning/hyperparameters) for more details.
- batchSize
The batch size to use for training. The batch size is the number of training examples used to train a single forward and backward pass.
- classificationNClasses
The number of classes to use for computing classification metrics.
- classificationPositiveClass
The positive class to use for computing classification metrics.
- computeClassificationMetrics
The classification metrics to compute using the validation dataset at the end of every epoch.
- learningRateMultiplier
The learning rate multiplier to use for training.
- nEpochs
The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
- promptLossWeight
The weight to use for loss on the prompt tokens.
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- new Hyperparams(batchSize: Int, classificationNClasses: Optional[Int] = Optional.Absent, classificationPositiveClass: Optional[String] = Optional.Absent, computeClassificationMetrics: Optional[Boolean] = Optional.Absent, learningRateMultiplier: Double, nEpochs: Int, promptLossWeight: Double)
- batchSize
The batch size to use for training. The batch size is the number of training examples used to train a single forward and backward pass.
- classificationNClasses
The number of classes to use for computing classification metrics.
- classificationPositiveClass
The positive class to use for computing classification metrics.
- computeClassificationMetrics
The classification metrics to compute using the validation dataset at the end of every epoch.
- learningRateMultiplier
The learning rate multiplier to use for training.
- nEpochs
The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
- promptLossWeight
The weight to use for loss on the prompt tokens.
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- val batchSize: Int
- val classificationNClasses: Optional[Int]
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