📄 mrpc¶

Note

ID: cards.mrpc | Type: TaskCard

{
    "loader": {
        "name": "mrpc",
        "path": "glue",
        "streaming": false,
        "type": "load_hf"
    },
    "preprocess_steps": [
        "splitters.default",
        {
            "mappers": {
                "label": {
                    "0": "not equivalent",
                    "1": "equivalent"
                }
            },
            "type": "map_instance_values"
        },
        {
            "fields": {
                "choices": [
                    "not equivalent",
                    "equivalent"
                ]
            },
            "type": "add_fields"
        }
    ],
    "task": {
        "inputs": [
            "choices",
            "sentence1",
            "sentence2"
        ],
        "metrics": [
            "metrics.accuracy"
        ],
        "outputs": [
            "label"
        ],
        "type": "form_task"
    },
    "templates": {
        "items": [
            {
                "input_format": "Given this sentence: {sentence1}, classify if this sentence: {sentence2} is {choices}.",
                "output_format": "{label}",
                "type": "input_output_template"
            }
        ],
        "type": "templates_list"
    },
    "type": "task_card"
}

Explanation about TaskCard¶

TaskCard delineates the phases in transforming the source dataset into a model-input, and specifies the metrics for evaluation of model-output.

Attributes:
loader: specifies the source address and the loading operator that can access that source and transform it into a unitxt multistream.

preprocess_steps: list of unitxt operators to process the data source into a model-input.

task: specifies the fields (of the already (pre)processed instance) making the inputs, the fields making the outputs, and the metrics to be used for evaluating the model output.

templates: format strings to be applied on the input fields (specified by the task) and the output fields. The template also carries the instructions and the list of postprocessing steps, to be applied to the model output.

Explanation about FormTask¶

FormTask packs the different instance fields into dictionaries by their roles in the task.

Attributes:

inputs (Union[Dict[str, str], List[str]]):
Dictionary with string names of instance input fields and types of respective values. In case a list is passed, each type will be assumed to be Any.

outputs (Union[Dict[str, str], List[str]]):
Dictionary with string names of instance output fields and types of respective values. In case a list is passed, each type will be assumed to be Any.

metrics (List[str]): List of names of metrics to be used in the task. prediction_type (Optional[str]):

Need to be consistent with all used metrics. Defaults to None, which means that it will be set to Any.

The output instance contains three fields:
“inputs” whose value is a sub-dictionary of the input instance, consisting of all the fields listed in Arg ‘inputs’. “outputs” – for the fields listed in Arg “outputs”. “metrics” – to contain the value of Arg ‘metrics’

Explanation about InputOutputTemplate¶

Generate field ‘source’ from fields designated as input, and fields ‘target’ and ‘references’ from fields designated as output, of the processed instance.

Args specify the formatting strings with which to glue together the input and output designated fields of the processed instance into one string (‘source’ and ‘target’), and into a list of strings (‘references’).

Explanation about AddFields¶

Adds specified fields to each instance in a given stream or all streams (default) If fields exist, updates them.

Args:

fields (Dict[str, object]): The fields to add to each instance.
Use ‘/’ to access inner fields

use_deepcopy (bool) : Deep copy the input value to avoid later modifications

Examples:
# Add a ‘classes’ field with a value of a list “positive” and “negative” to all streams AddFields(fields={“classes”: [“positive”,”negatives”]})

# Add a ‘start’ field under the ‘span’ field with a value of 0 to all streams AddFields(fields={“span/start”: 0}

# Add a ‘classes’ field with a value of a list “positive” and “negative” to ‘train’ stream AddFields(fields={“classes”: [“positive”,”negatives”], apply_to_stream=[“train”]})

# Add a ‘classes’ field on a given list, prevent modification of original list # from changing the instance. AddFields(fields={“classes”: alist}), use_deepcopy=True) # if now alist is modified, still the instances remain intact.

Explanation about MapInstanceValues¶

A class used to map instance values into other values.

This class is a type of StreamInstanceOperator, it maps values of instances in a stream using predefined mappers.

Attributes:

mappers (Dict[str, Dict[str, str]]): The mappers to use for mapping instance values.
Keys are the names of the fields to be mapped, and values are dictionaries that define the mapping from old values to new values.

strict (bool): If True, the mapping is applied strictly. That means if a value
does not exist in the mapper, it will raise a KeyError. If False, values that are not present in the mapper are kept as they are.

process_every_value (bool): If True, all fields to be mapped should be lists, and the mapping
is to be applied to their individual elements. If False, mapping is only applied to a field containing a single value.

Examples:
MapInstanceValues(mappers={“a”: {“1”: “hi”, “2”: “bye”}}) replaces ‘1’ with ‘hi’ and ‘2’ with ‘bye’ in field ‘a’ in all instances of all streams: instance {“a”:”1”, “b”: 2} becomes {“a”:”hi”, “b”: 2}.

MapInstanceValues(mappers={“a”: {“1”: “hi”, “2”: “bye”}}, process_every_element=True) Assuming field ‘a’ is a list of values, potentially including “1”-s and “2”-s, this replaces each such “1” with “hi” and “2” – with “bye” in all instances of all streams: instance {“a”: [“1”, “2”], “b”: 2} becomes {“a”: [“hi”, “bye”], “b”: 2}.

MapInstanceValues(mappers={“a”: {“1”: “hi”, “2”: “bye”}}, strict=True) To ensure that all values of field ‘a’ are mapped in every instance, use strict=True. Input instance {“a”:”3”, “b”: 2} will raise an exception per the above call, because “3” is not a key in the mapper of “a”.

MapInstanceValues(mappers={“a”: {str([1,2,3,4]): ‘All’, str([]): ‘None’}}, strict=True) replaces a list [1,2,3,4] with the string ‘All’ and an empty list by string ‘None’. Note that mapped values are defined by their string representation, so mapped values must be converted to strings.

References: splitters.default, metrics.accuracy