📄 Claim Stance Topic¶

Claim Stance contains 2,394 labeled Wikipedia claims for 55 topics. The dataset includes the stance (Pro/Con) of each claim towards the topic, as well as fine-grained annotations, based on the semantic model of Stance Classification of Context-Dependent Claims (topic target, topic sentiment towards its target, claim target, claim sentiment towards its target, and the relation between the targets)… See the full description on the dataset page: https://huggingface.co/datasets/ibm/claim_stance.

Tags: language:en, license:cc-by-3.0, region:us, size_categories:1K<n<10K, task_categories:text-classification, category:dataset

cards.claim_stance_topic

TaskCard(
    loader=LoadHF(
        path="ibm/claim_stance",
        name="claim_stance_topic",
    ),
    preprocess_steps=[
        Set(
            fields={
                "classes": [
                    "advertising",
                    "all nations a right to nuclear weapons",
                    "a mandatory retirement age",
                    "american jobs act",
                    "asean",
                    "atheism",
                    "austerity measures",
                    "barrier methods of contraception",
                    "blasphemy",
                    "boxing",
                    "bribery",
                    "burning the stars and stripes",
                    "children",
                    "collective bargaining rights claimed by trades unions",
                    "congressional earmarks",
                    "democratic governments should require voters to present photo identification at the polling station",
                    "democratization",
                    "endangered species",
                    "enforce term limits on the legislative branch of government",
                    "freedom of speech",
                    "fund education using a voucher scheme",
                    "gambling",
                    "governments should choose open source software",
                    "high rises for housing",
                    "holocaust denial",
                    "housewives should be paid for their work",
                    "hydroelectric dams",
                    "implement playoffs in collegiate level american football",
                    "intellectual property rights",
                    "israel's 2008-2009 military operations against gaza",
                    "leaking of military documents",
                    "multiculturalism",
                    "national service",
                    "only teach abstinence for sex education in schools",
                    "open primaries",
                    "partial birth abortions",
                    "physical education",
                    "poor communities",
                    "raising the school leaving age to 18",
                    "re-engage with myanmar",
                    "the blockade of gaza",
                    "the creation of private universities in the uk",
                    "the free market",
                    "the growing of tobacco",
                    "the keystone xl pipeline",
                    "the monarchy",
                    "the one-child policy of the republic of china",
                    "the right to asylum",
                    "the right to bear arms",
                    "the sale of violent video games to minors",
                    "the use of affirmative action",
                    "the use of performance enhancing drugs in professional sports",
                    "the use of truth and reconciliation commissions",
                    "wind power",
                    "year round schooling",
                ],
                "text_type": "argument",
            },
        ),
    ],
    task="tasks.classification.multi_class.topic_classification",
    templates="templates.classification.multi_class.all",
)

[source]

from unitxt.loaders import LoadHF
from unitxt.operators import Set

Explanation about TaskCard¶

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

Args:

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 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 LoadHF¶

Loads datasets from the HuggingFace Hub.

It supports loading with or without streaming, and it 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. When you provide a list of data_files to Hugging Face’s load_dataset function without explicitly specifying the split argument, these files are automatically placed into the train split.

revision:
Optional. The revision of the dataset. Often the commit id. Use in case you want to set the dataset version.

streaming (bool):
indicating if streaming should be used.

filtering_lambda (str, optional):
A lambda function for filtering the data after loading.

num_proc (int, optional):
Specifies 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 Set¶

Sets specified fields in each instance, in a given stream or all streams (default), with specified values. If fields exist, updates them, if do not exist – adds 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:
# Set a value of a list consisting of “positive” and “negative” do field “classes” to each and every instance of all streams Set(fields={"classes": ["positive","negatives"]})

# In each and every instance of all streams, field “span” is to become a dictionary containing a field “start”, in which the value 0 is to be set Set(fields={"span/start": 0}

# In all instances of stream “train” only, Set field “classes” to have the value of a list consisting of “positive” and “negative” Set(fields={"classes": ["positive","negatives"], apply_to_stream=["train"]})

# Set field “classes” to have the value of a given list, preventing modification of original list from changing the instance. Set(fields={"classes": alist}), use_deepcopy=True) if now alist is modified, still the instances remain intact.

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