This can be a change of state, e.g., enqueued, started, failed or completed, or other events like uploaded results. The operation cannot be executed in the current resource's state.Įrror content as defined in the Microsoft REST guidelinesĪ timestamp when this event was created (in unix epochs). The request data is invalid for this operation. The operation is forbidden for the current user/api key. The requested operation conflicts with the current resource state. The location where the error happened if available. Inner error as defined in the Microsoft REST guidelines "id": "ft-72a2792ef7d24ba7b82c7fe4a37e379f",Įrror codes as defined in the Microsoft REST guidelines The fine tune has been successfully created.Įxamples Creating a fine tune job for classification.Ĭreating a fine tune job for classification. The file identity (file-id) that is used to evaluate the fine tuned model during training. The suffix can contain up to 40 characters (a-z, A-Z, 0-9,- and _) that will be added to your fine-tuned model name. The suffix used to identify the fine-tuned model. If prompts are extremely long (relative to completions), it may make sense to reduce this weight so as to avoid over-prioritizing learning the prompt. (as compared to the completion which always has a weight of 1.0), and can add a stabilizing effect to training when completions are short. This controls how much the model tries to learn to generate the prompt The weight to use for loss on the prompt tokens.
#Merlin finetunes full
An epoch refers to one full cycle through the training dataset. The number of epochs to train the model for. We recommend experimenting with values in the range 0.02 to 0.2 to see what produces the best results. Larger learning rates tend to perform better with larger batch sizes. The fine-tuning learning rate is the original learning rate used for pre-training multiplied by this value. The learning rate multiplier to use for training. You must specify classification_n_classes for multiclass classification or classification_positive_class for binary classification. In order to compute classification metrics, you must provide a validation_file.Additionally, These metrics can be viewed in the results file. If set, we calculate classification-specific metrics such as accuracy and F-1 score using the validation set at the end of every epoch. This parameter is needed to generate precision, recall, and F1 metrics when doing binary classification.Ī value indicating whether to compute classification metrics.
The positive class in binary classification. This parameter is required for multiclass classification. The number of classes in a classification task. A smaller beta score puts more weight on precision and less on recall. With a beta of 1 (i.e.the F-1 score), precision and recall are given the same weight.Ī larger beta score puts more weight on recall and less on precision. This is only used for binary classification. The F-beta score is a generalization of F-1 score.
If this is provided, we calculate F-beta scores at the specified beta values. The default value as well as the maximum value for this property are specific to a base model. In general, we've found that larger batch sizes tend to work better for larger datasets. The batch size is the number of training examples used to train a single forward and backward pass. The file identity (file-id) that is used for training this fine tuned model. The identifier (model-id) of the base model used for this fine-tune. Provide your Cognitive Services Azure OpenAI account key here.
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