Constructor Detail

Method Detail

• batchSize

   final FineTuningJobCreateParams.Hyperparameters.Builder batchSize(FineTuningJobCreateParams.Hyperparameters.BatchSize batchSize)

  Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

• batchSize

   final FineTuningJobCreateParams.Hyperparameters.Builder batchSize(JsonField<FineTuningJobCreateParams.Hyperparameters.BatchSize> batchSize)

  Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

• batchSize

   final FineTuningJobCreateParams.Hyperparameters.Builder batchSize(Long integer)

  Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

• batchSizeAuto

   final FineTuningJobCreateParams.Hyperparameters.Builder batchSizeAuto()

  Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

• learningRateMultiplier

   final FineTuningJobCreateParams.Hyperparameters.Builder learningRateMultiplier(FineTuningJobCreateParams.Hyperparameters.LearningRateMultiplier learningRateMultiplier)

  Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

• learningRateMultiplier

   final FineTuningJobCreateParams.Hyperparameters.Builder learningRateMultiplier(JsonField<FineTuningJobCreateParams.Hyperparameters.LearningRateMultiplier> learningRateMultiplier)

  Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

• learningRateMultiplier

   final FineTuningJobCreateParams.Hyperparameters.Builder learningRateMultiplier(Double number)

  Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

• learningRateMultiplierAuto

   final FineTuningJobCreateParams.Hyperparameters.Builder learningRateMultiplierAuto()

  Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

• nEpochs

   final FineTuningJobCreateParams.Hyperparameters.Builder nEpochs(FineTuningJobCreateParams.Hyperparameters.NEpochs nEpochs)

  The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

• nEpochs

   final FineTuningJobCreateParams.Hyperparameters.Builder nEpochs(JsonField<FineTuningJobCreateParams.Hyperparameters.NEpochs> nEpochs)

  The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

• nEpochs

   final FineTuningJobCreateParams.Hyperparameters.Builder nEpochs(Long integer)

  The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

• nEpochsAuto

   final FineTuningJobCreateParams.Hyperparameters.Builder nEpochsAuto()

  The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

• additionalProperties

   final FineTuningJobCreateParams.Hyperparameters.Builder additionalProperties(Map<String, JsonValue> additionalProperties)

• putAdditionalProperty

   final FineTuningJobCreateParams.Hyperparameters.Builder putAdditionalProperty(String key, JsonValue value)

• putAllAdditionalProperties

   final FineTuningJobCreateParams.Hyperparameters.Builder putAllAdditionalProperties(Map<String, JsonValue> additionalProperties)

• removeAdditionalProperty

   final FineTuningJobCreateParams.Hyperparameters.Builder removeAdditionalProperty(String key)

• removeAllAdditionalProperties

   final FineTuningJobCreateParams.Hyperparameters.Builder removeAllAdditionalProperties(Set<String> keys)

• build

   final FineTuningJobCreateParams.Hyperparameters build()