📄 Argument Topic¶
Argument Quality Ranking The dataset contains 30,497 crowd-sourced arguments for 71 debatable topics labeled for quality and stance, split into train, validation and test sets. The dataset was originally published as part of our paper: A Large-scale Dataset for Argument Quality Ranking: Construction and Analysis. Argument Topic This subset contains 9,487 of the… See the full description on the dataset page: https://huggingface.co/datasets/ibm/argument_quality_ranking_30k.
Tags: arxiv:1911.11408, language:en, license:cc-by-3.0, region:us, size_categories:10K<n<100K, task_categories:text-classification, category:dataset
cards.argument_topic
TaskCard(
loader=LoadHF(
path="ibm/argument_quality_ranking_30k",
name="argument_topic",
),
preprocess_steps=[
Set(
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",
},
),
],
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
Read more about catalog usage here.