📄 Xs¶

WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a fill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires commonsense reasoning… See the full description on the dataset page: https://huggingface.co/datasets/winogrande

Tags: language:en, region:us

Note

ID: cards.winogrande.xs | Type: TaskCard

{
    "__description__": "WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a fill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires commonsense reasoning… See the full description on the dataset page: https://huggingface.co/datasets/winogrande",
    "__tags__": {
        "language": "en",
        "region": "us"
    },
    "__type__": "task_card",
    "loader": {
        "__type__": "load_hf",
        "name": "winogrande_xs",
        "path": "winogrande"
    },
    "preprocess_steps": [
        "splitters.small_no_test",
        {
            "__type__": "list_field_values",
            "fields": [
                "option1",
                "option2"
            ],
            "to_field": "choices"
        },
        {
            "__type__": "cast_fields",
            "fields": {
                "answer": "int"
            }
        },
        {
            "__type__": "add_constant",
            "add": -1,
            "field": "answer"
        },
        {
            "__type__": "rename_fields",
            "field_to_field": {
                "sentence": "question"
            }
        }
    ],
    "task": "tasks.qa.multiple_choice.open",
    "templates": "templates.qa.multiple_choice.open.all"
}

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 AddConstant¶

Adds a constant, being argument ‘add’, to the processed value.

Args:
add: the constant to add.

Explanation about LoadHF¶

Loads datasets from the Huggingface Hub.

It supports loading with or without streaming, and can filter datasets upon loading.
Args:
path: The path or identifier of the dataset on the Huggingface Hub. name: An optional dataset name. data_dir: Optional directory to store downloaded data. split: Optional specification of which split to load. data_files: Optional specification of particular data files to load. streaming: Bool indicating if streaming should be used. filtering_lambda: A lambda function for filtering the data after loading. num_proc: Optional integer to specify the number of processes to use for parallel dataset loading.

Example:
Loading glue’s mrpc dataset
load_hf = LoadHF(path='glue', name='mrpc')

Explanation about ListFieldValues¶

Concatenates values of multiple fields into a list, and assigns it to a new field.

Explanation about CastFields¶

Casts specified fields to specified types.

Args:
use_nested_query (bool): Whether to cast nested fields, expressed in dpath. Defaults to False. fields (Dict[str, str]): A dictionary mapping field names to the names of the types to cast the fields to.

e.g: “int”, “str”, “float”, “bool”. Basic names of types

defaults (Dict[str, object]): A dictionary mapping field names to default values for cases of casting failure. process_every_value (bool): If true, all fields involved must contain lists, and each value in the list is then casted. Defaults to False.

Examples:

CastFields(

fields={“a/d”: “float”, “b”: “int”}, failure_defaults={“a/d”: 0.0, “b”: 0}, process_every_value=True, use_nested_query=True

)

would process the input instance: {“a”: {“d”: [“half”, “0.6”, 1, 12]}, “b”: [“2”]}
into {“a”: {“d”: [0.0, 0.6, 1.0, 12.0]}, “b”: [2]}

Explanation about RenameFields¶

Renames fields.

Move value from one field to another, potentially, if field name contains a /, from one branch into another. Remove the from field, potentially part of it in case of / in from_field.

Examples:
RenameFields(field_to_field={“b”: “c”}) will change inputs [{“a”: 1, “b”: 2}, {“a”: 2, “b”: 3}] to [{“a”: 1, “c”: 2}, {“a”: 2, “c”: 3}]

RenameFields(field_to_field={“b”: “c/d”}) will change inputs [{“a”: 1, “b”: 2}, {“a”: 2, “b”: 3}] to [{“a”: 1, “c”: {“d”: 2}}, {“a”: 2, “c”: {“d”: 3}}]

RenameFields(field_to_field={“b”: “b/d”}) will change inputs [{“a”: 1, “b”: 2}, {“a”: 2, “b”: 3}] to [{“a”: 1, “b”: {“d”: 2}}, {“a”: 2, “b”: {“d”: 3}}]

RenameFields(field_to_field={“b/c/e”: “b/d”}) will change inputs [{“a”: 1, “b”: {“c”: {“e”: 2, “f”: 20}}}] to [{“a”: 1, “b”: {“c”: {“f”: 20}, “d”: 2}}]

References: splitters.small_no_test, templates.qa.multiple_choice.open.all, tasks.qa.multiple_choice.open