📄 argument_topic¶

Note

ID: cards.argument_topic | Type: TaskCard

{
    "loader": {
        "name": "argument_topic",
        "path": "ibm/argument_quality_ranking_30k",
        "type": "load_hf"
    },
    "preprocess_steps": [
        {
            "fields": {
                "classes": [
                    "affirmative action",
                    "algorithmic trading",
                    "assisted suicide",
                    "atheism",
                    "austerity regime",
                    "blockade of the gaza strip",
                    "cancel pride parades",
                    "cannabis",
                    "capital punishment",
                    "collectivism",
                    "compulsory voting",
                    "cosmetic surgery",
                    "cosmetic surgery for minors",
                    "embryonic stem cell research",
                    "entrapment",
                    "executive compensation",
                    "factory farming",
                    "fast food",
                    "fight urbanization",
                    "flag burning",
                    "foster care",
                    "gender-neutral language",
                    "guantanamo bay detention camp",
                    "holocaust denial",
                    "homeopathy",
                    "homeschooling",
                    "human cloning",
                    "intellectual property rights",
                    "intelligence tests",
                    "journalism",
                    "judicial activism",
                    "libertarianism",
                    "marriage",
                    "missionary work",
                    "multi-party system",
                    "naturopathy",
                    "organ trade",
                    "payday loans",
                    "polygamy",
                    "private military companies",
                    "prostitution",
                    "racial profiling",
                    "retirement",
                    "safe spaces",
                    "school prayer",
                    "sex selection",
                    "social media",
                    "space exploration",
                    "stay-at-home dads",
                    "student loans",
                    "surrogacy",
                    "targeted killing",
                    "telemarketing",
                    "television",
                    "the abolition of nuclear weapons",
                    "the church of scientology",
                    "the development of autonomous cars",
                    "the olympic games",
                    "the right to keep and bear arms",
                    "the three-strikes laws",
                    "the use of child actors",
                    "the use of economic sanctions",
                    "the use of public defenders",
                    "the use of school uniform",
                    "the vow of celibacy",
                    "vocational education",
                    "whaling",
                    "wikipedia",
                    "women in combat",
                    "zero-tolerance policy in schools",
                    "zoos"
                ],
                "text_type": "argument",
                "type_of_class": "topic"
            },
            "type": "add_fields"
        }
    ],
    "task": "tasks.classification.multi_class",
    "templates": "templates.classification.multi_class.all",
    "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 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.

References: templates.classification.multi_class.all, tasks.classification.multi_class