Usage
The following example shows how the step can be used in a recipe.Examples
Examples
The step has no required parameters, so the simplest call is simply
Inputs & Outputs
The following are the inputs expected by the step and the outputs it produces. These are generally columns (ds.first_name), datasets (ds or ds[["first_name", "last_name"]]) or models (referenced
by name e.g. "churn-clf").
Inputs
Inputs
A column of URLs to images to predict captions for.
Outputs
Outputs
Configuration
The following parameters can be used to configure the behaviour of the step by including them in a json object as the last “input” to the step, i.e.step(..., {"param": "value", ...}) -> (output).
Parameters
Parameters
Which projection model to use.
The projection model maps embeddings from the pretrained Clip image model, to the pretrained
GPT-2 language model. Select between a multi-layer perceptron (“MLP”), or the faster transformer
(“TRF”).Values must be one of the following:
TRFMLP
Select the parameter set for the model.
The ClipClap authors provide weights for models having been trained either on the
COCO dataset (“coco”) or the ConceptualCaptions
dataset (“concept”).Values must be one of the following:
cococoncept
Whether to use beam-search or greedy word prediction.
When enabled, uses a more expensive but “smarter” algorithm to predict the words in the captions.