[+] expand all

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.