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Stateless model reference

.model files are used in stateless model evaluation. These are files with ranking expressions, located in models / a subdirectory of models, with .model suffix:

├── models
│   └── my_model.model
└── services.xml

.model file format specification

model [name] {

    inputs {
        ([input-name] [input-type])*
    }

    constants {
        constant*
    }

    (function [name]([argument-name]*) {
        expression: [ranking expression]
    })*

}

The elements can appear in any order (and number).

Constant element

[constant-name] [type]?: [scalar, tensor on literal form, or file: followed by a file reference]
Name Description
name The name of the constant, written either the full feature name constant(myName), or just as name.
type The type of the constant, either double or a tensor type. If omitted, the type is double.
value A number, a tensor on literal form, or file: followed by a path relative to the model file to a file containing the constant. The file must be stored on the tensor JSON Format and end with .json. The file may be lz4 compressed, in which case the ending must be .json.lz4.

Constant examples:

constants {
    myDouble: 0.5
    constant(myOtherDouble) double: 0.6
    constant(myArray) tensor(x[3]):[1, 2, 3]
    constant(myMap) tensor(key{}]):{key1: 1.0, key2: 2.0}
    constant(myLargeTensor) tensor(x[10000]): file:constants/myTensor.json.lz4
}

Model example

This file must be saved as example.model somewhere in the models directory tree, and the same directory must also contain myLargeConstant.json.lz4 with a tensor as compressed json.

model example {

    # All inputs that are not scalar (aka 0-dimensional tensor) must be declared
    inputs {
        input1 tensor(name{},x[3])
        input2 tensor(x[3])
    }

    constants {
        constant(constant1) tensor(x[3]):{{x:0}:0.5, {x:1}:1.5, {x:2}:2.5}
        constant(constant2): 3.0
        constant(myLargeConstant) tensor(x[10000]): file:myLargeConstant.json.lz4
    }

    function foo1() {
        expression: reduce(sum(input1 * input2, name) * constant1, max, x) * constant2
    }

    function foo2() {
        expression: reduce(sum(input1 * input2, name) * constant(constant1asLarge), max, x) * constant2
    }

}

This makes the model example available with the functions foo1 and foo2.