📄 With Context¶

tasks.qa.multiple_choice.with_context

Task(
    input_fields={
        "context": "Union[Text, Image, Audio, Table, Dialog, Video]",
        "context_type": "str",
        "question": "str",
        "choices": "List[str]",
    },
    reference_fields={
        "answer": "Union[int, str]",
        "choices": "List[str]",
    },
    prediction_type="str",
    augmentable_inputs=[
        "context",
        "question",
    ],
    metrics=[
        "metrics.accuracy",
    ],
)
[source]

Explanation about Task¶

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

Args:

input_fields (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.

reference_fields (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.

defaults (Optional[Dict[str, Any]]):
An optional dictionary with default values for chosen input/output keys. Needs to be consistent with names and types provided in ‘input_fields’ and/or ‘output_fields’ arguments. Will not overwrite values if already provided in a given instance.

The output instance contains three fields:

“input_fields” whose value is a sub-dictionary of the input instance, consisting of all the fields listed in Arg ‘input_fields’.

“reference_fields” – for the fields listed in Arg “reference_fields”.

“metrics” – to contain the value of Arg ‘metrics’

References: metrics.accuracy